library(dummies)
dummies-1.5.6 provided by Decision Patterns

Warning message:
package ‘gplots’ was built under R version 3.5.2 
library(ltm)
Loading required package: MASS
Loading required package: msm
Loading required package: polycor
library(dplyr)
package ‘dplyr’ was built under R version 3.5.2
Attaching package: ‘dplyr’

The following object is masked from ‘package:MASS’:

    select

The following objects are masked from ‘package:stats’:

    filter, lag

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    intersect, setdiff, setequal, union
library(readxl)
package ‘readxl’ was built under R version 3.5.2
library(psych)
package ‘psych’ was built under R version 3.5.2
Attaching package: ‘psych’

The following object is masked from ‘package:ltm’:

    factor.scores

The following object is masked from ‘package:polycor’:

    polyserial
require(MASS)
library(FNN)
package ‘FNN’ was built under R version 3.5.2
library(adabag)
Loading required package: rpart
Loading required package: caret
Loading required package: lattice
Loading required package: ggplot2

Attaching package: ‘ggplot2’

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    %+%, alpha

Loading required package: foreach
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Loading required package: parallel

Attaching package: ‘adabag’

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    margins
library(rpart) 
library(caret)
library(randomForest)
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.

Attaching package: ‘randomForest’

The following object is masked from ‘package:ggplot2’:

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    outlier

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library(party)
package ‘party’ was built under R version 3.5.2Loading required package: grid
Loading required package: mvtnorm
package ‘mvtnorm’ was built under R version 3.5.2Loading required package: modeltools
Loading required package: stats4
Loading required package: strucchange
Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Loading required package: sandwich
library(ROCR)
library(ggplot2)
library(rpart.plot)
nrow(bank)
[1] 45211

checking the missing values in the dataset

sum(is.na(bank))
[1] 0

Performing OverSampling to Data as the number of success cases are very less as compared to the failure cases

success <- bank[bank$y == "yes",]
success

nrow(success)
[1] 5289
failure <- bank[bank$y == "no",]
failure

nrow(failure)
[1] 39922
summary(bank_data)
      age                 job           marital         education    default   
 Min.   :18.00   management :1170   divorced: 594   primary  : 688   no :5222  
 1st Qu.:32.00   blue-collar: 957   married :3023   secondary:2618   yes:  66  
 Median :39.00   technician : 870   single  :1671   tertiary :1728             
 Mean   :41.49   admin.     : 613                   unknown  : 254             
 3rd Qu.:49.00   services   : 440                                              
 Max.   :95.00   retired    : 390                                              
                 (Other)    : 848                                              
    balance      housing     loan           contact          day            month     
 Min.   :-3058   no :2792   no :4581   cellular :3801   Min.   : 1.00   may    :1346  
 1st Qu.:  133   yes:2496   yes: 707   telephone: 393   1st Qu.: 8.00   jul    : 749  
 Median :  577                         unknown  :1094   Median :15.00   aug    : 695  
 Mean   : 1571                                          Mean   :15.47   jun    : 577  
 3rd Qu.: 1762                                          3rd Qu.:21.00   apr    : 441  
 Max.   :81204                                          Max.   :31.00   nov    : 405  
                                                                        (Other):1075  
    duration       campaign          pdays           previous          poutcome      y       
 Min.   :   2   Min.   : 1.000   Min.   : -1.00   Min.   : 0.0000   failure: 580   no :2644  
 1st Qu.: 139   1st Qu.: 1.000   1st Qu.: -1.00   1st Qu.: 0.0000   other  : 269   yes:2644  
 Median : 256   Median : 2.000   Median : -1.00   Median : 0.0000   success: 545             
 Mean   : 374   Mean   : 2.547   Mean   : 52.76   Mean   : 0.8907   unknown:3894             
 3rd Qu.: 494   3rd Qu.: 3.000   3rd Qu.: 78.00   3rd Qu.: 1.0000                            
 Max.   :3881   Max.   :41.000   Max.   :871.00   Max.   :58.0000                            
                                                                                             

Converting Job variable into dummy variable

cbind(bank_data,dummy(bank_data$job, sep = "_"))

job_dummy <- dummy(bank_data$job, sep = "_")
job_dummy
        job_admin. job_blue-collar job_entrepreneur job_housemaid job_management job_retired
   [1,]          0               0                0             0              0           0
   [2,]          1               0                0             0              0           0
   [3,]          0               0                0             0              1           0
   [4,]          0               0                0             0              0           0
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   [6,]          0               0                0             0              0           0
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   [8,]          0               0                0             0              0           1
   [9,]          1               0                0             0              0           0
  [10,]          1               0                0             0              0           0
  [11,]          1               0                0             0              0           0
  [12,]          0               0                0             0              1           0
  [13,]          0               0                0             0              1           0
  [14,]          0               0                0             0              0           1
  [15,]          0               0                0             0              1           0
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  [22,]          0               0                0             0              1           0
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  [24,]          1               0                0             0              0           0
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  [28,]          0               0                0             0              1           0
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  [30,]          0               0                0             0              0           1
  [31,]          0               0                0             0              0           0
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  [33,]          0               1                0             0              0           0
  [34,]          0               0                0             0              1           0
  [35,]          0               0                0             0              0           1
  [36,]          0               0                0             0              1           0
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  [41,]          1               0                0             0              0           0
  [42,]          0               0                0             0              0           1
  [43,]          0               0                0             0              0           0
  [44,]          0               0                0             0              0           0
  [45,]          0               0                0             0              0           0
  [46,]          0               1                0             0              0           0
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  [50,]          0               0                0             0              0           1
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  [59,]          0               0                0             0              1           0
  [60,]          0               0                0             0              1           0
  [61,]          0               0                0             0              0           1
  [62,]          0               0                0             0              1           0
  [63,]          0               0                0             0              0           0
  [64,]          0               0                0             0              0           0
  [65,]          0               0                0             0              1           0
  [66,]          0               0                0             0              1           0
  [67,]          0               1                0             0              0           0
  [68,]          0               0                0             0              0           1
  [69,]          0               1                0             0              0           0
  [70,]          0               0                0             0              1           0
  [71,]          1               0                0             0              0           0
  [72,]          0               0                0             0              0           0
  [73,]          1               0                0             0              0           0
  [74,]          0               0                0             0              1           0
  [75,]          0               0                0             0              1           0
  [76,]          0               0                0             0              0           0
  [77,]          0               0                0             0              0           0
  [78,]          0               0                0             0              0           1
  [79,]          0               0                0             0              0           0
  [80,]          0               0                0             0              0           0
  [81,]          0               0                0             0              1           0
  [82,]          0               0                0             0              1           0
  [83,]          0               0                0             0              0           1
        job_self-employed job_services job_student job_technician job_unemployed job_unknown
   [1,]                 0            0           0              0              1           0
   [2,]                 0            0           0              0              0           0
   [3,]                 0            0           0              0              0           0
   [4,]                 0            0           1              0              0           0
   [5,]                 0            0           0              1              0           0
   [6,]                 0            0           0              0              0           1
   [7,]                 0            0           0              0              1           0
   [8,]                 0            0           0              0              0           0
   [9,]                 0            0           0              0              0           0
  [10,]                 0            0           0              0              0           0
  [11,]                 0            0           0              0              0           0
  [12,]                 0            0           0              0              0           0
  [13,]                 0            0           0              0              0           0
  [14,]                 0            0           0              0              0           0
  [15,]                 0            0           0              0              0           0
  [16,]                 0            0           0              0              0           0
  [17,]                 0            0           1              0              0           0
  [18,]                 0            0           0              0              0           0
  [19,]                 0            0           0              1              0           0
  [20,]                 0            0           1              0              0           0
  [21,]                 0            0           0              1              0           0
  [22,]                 0            0           0              0              0           0
  [23,]                 0            0           0              1              0           0
  [24,]                 0            0           0              0              0           0
  [25,]                 0            0           0              0              0           0
  [26,]                 0            0           0              0              0           0
  [27,]                 0            0           0              0              0           0
  [28,]                 0            0           0              0              0           0
  [29,]                 0            0           0              1              0           0
  [30,]                 0            0           0              0              0           0
  [31,]                 0            1           0              0              0           0
  [32,]                 0            0           0              0              0           1
  [33,]                 0            0           0              0              0           0
  [34,]                 0            0           0              0              0           0
  [35,]                 0            0           0              0              0           0
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  [56,]                 0            1           0              0              0           0
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  [63,]                 0            1           0              0              0           0
  [64,]                 0            0           0              0              1           0
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  [72,]                 0            0           0              1              0           0
  [73,]                 0            0           0              0              0           0
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  [76,]                 1            0           0              0              0           0
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  [83,]                 0            0           0              0              0           0
 [ reached getOption("max.print") -- omitted 5205 rows ]
bank_data$job <- NULL
bank_data

Converting Marital variable into dummy variable

cbind(bank_data,dummy(bank_data$marital, sep = "_"))

marital_dummy <- dummy(bank_data$marital, sep = "_")
marital_dummy
        marital_divorced marital_married marital_single
   [1,]                0               0              1
   [2,]                0               0              1
   [3,]                0               1              0
   [4,]                0               0              1
   [5,]                0               1              0
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  [22,]                1               0              0
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  [25,]                0               0              1
  [26,]                1               0              0
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  [29,]                0               0              1
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 [271,]                0               1              0
 [272,]                0               1              0
 [273,]                0               1              0
 [274,]                1               0              0
 [275,]                0               1              0
 [276,]                1               0              0
 [277,]                0               0              1
 [278,]                0               0              1
 [279,]                0               1              0
 [280,]                0               1              0
 [281,]                0               0              1
 [282,]                0               0              1
 [283,]                0               0              1
 [284,]                0               0              1
 [285,]                1               0              0
 [286,]                0               0              1
 [287,]                0               1              0
 [288,]                0               1              0
 [289,]                0               0              1
 [290,]                0               1              0
 [291,]                1               0              0
 [292,]                0               1              0
 [293,]                0               0              1
 [294,]                0               0              1
 [295,]                1               0              0
 [296,]                0               1              0
 [297,]                0               0              1
 [298,]                0               0              1
 [299,]                0               0              1
 [300,]                0               0              1
 [301,]                1               0              0
 [302,]                0               0              1
 [303,]                0               1              0
 [304,]                0               1              0
 [305,]                0               1              0
 [306,]                0               1              0
 [307,]                0               0              1
 [308,]                0               1              0
 [309,]                0               1              0
 [310,]                0               0              1
 [311,]                0               1              0
 [312,]                0               1              0
 [313,]                0               0              1
 [314,]                0               1              0
 [315,]                0               1              0
 [316,]                0               0              1
 [317,]                0               1              0
 [318,]                1               0              0
 [319,]                0               1              0
 [320,]                0               1              0
 [321,]                0               0              1
 [322,]                0               0              1
 [323,]                0               1              0
 [324,]                0               0              1
 [325,]                0               0              1
 [326,]                1               0              0
 [327,]                0               1              0
 [328,]                0               1              0
 [329,]                0               1              0
 [330,]                0               1              0
 [331,]                0               1              0
 [332,]                1               0              0
 [333,]                0               1              0
 [ reached getOption("max.print") -- omitted 4955 rows ]
bank_data$marital <- NULL
bank_data

Converting Education variable into dummy variable

cbind(bank_data,dummy(bank_data$education, sep = "_"))

education_dummy <- dummy(bank_data$education, sep = "_")
education_dummy
        education_primary education_secondary education_tertiary education_unknown
   [1,]                 0                   0                  1                 0
   [2,]                 0                   1                  0                 0
   [3,]                 0                   0                  1                 0
   [4,]                 0                   1                  0                 0
   [5,]                 0                   0                  1                 0
   [6,]                 0                   0                  0                 1
   [7,]                 0                   1                  0                 0
   [8,]                 1                   0                  0                 0
   [9,]                 0                   1                  0                 0
  [10,]                 0                   1                  0                 0
  [11,]                 0                   1                  0                 0
  [12,]                 0                   0                  1                 0
  [13,]                 1                   0                  0                 0
  [14,]                 1                   0                  0                 0
  [15,]                 0                   0                  1                 0
  [16,]                 0                   1                  0                 0
  [17,]                 1                   0                  0                 0
  [18,]                 0                   0                  1                 0
  [19,]                 0                   1                  0                 0
  [20,]                 0                   1                  0                 0
  [21,]                 0                   0                  1                 0
  [22,]                 0                   1                  0                 0
  [23,]                 0                   1                  0                 0
  [24,]                 0                   1                  0                 0
  [25,]                 0                   1                  0                 0
  [26,]                 0                   1                  0                 0
  [27,]                 0                   1                  0                 0
  [28,]                 1                   0                  0                 0
  [29,]                 0                   0                  1                 0
  [30,]                 1                   0                  0                 0
  [31,]                 0                   1                  0                 0
  [32,]                 0                   0                  0                 1
  [33,]                 0                   1                  0                 0
  [34,]                 0                   0                  1                 0
  [35,]                 1                   0                  0                 0
  [36,]                 0                   0                  1                 0
  [37,]                 0                   0                  1                 0
  [38,]                 0                   0                  1                 0
  [39,]                 0                   1                  0                 0
  [40,]                 0                   0                  0                 1
  [41,]                 0                   1                  0                 0
  [42,]                 0                   0                  1                 0
  [43,]                 0                   1                  0                 0
  [44,]                 0                   1                  0                 0
  [45,]                 0                   0                  1                 0
  [46,]                 0                   1                  0                 0
  [47,]                 0                   1                  0                 0
  [48,]                 1                   0                  0                 0
  [49,]                 0                   1                  0                 0
  [50,]                 1                   0                  0                 0
  [51,]                 0                   1                  0                 0
  [52,]                 0                   0                  1                 0
  [53,]                 0                   1                  0                 0
  [54,]                 0                   0                  1                 0
  [55,]                 0                   1                  0                 0
  [56,]                 0                   1                  0                 0
  [57,]                 0                   1                  0                 0
  [58,]                 0                   1                  0                 0
  [59,]                 0                   0                  1                 0
  [60,]                 0                   0                  1                 0
  [61,]                 1                   0                  0                 0
  [62,]                 0                   0                  1                 0
  [63,]                 0                   1                  0                 0
  [64,]                 0                   0                  1                 0
  [65,]                 0                   0                  1                 0
  [66,]                 0                   0                  1                 0
  [67,]                 1                   0                  0                 0
  [68,]                 0                   0                  0                 1
  [69,]                 0                   1                  0                 0
  [70,]                 0                   0                  1                 0
  [71,]                 0                   1                  0                 0
  [72,]                 0                   1                  0                 0
  [73,]                 0                   1                  0                 0
  [74,]                 0                   0                  1                 0
  [75,]                 0                   0                  1                 0
  [76,]                 0                   0                  1                 0
  [77,]                 0                   1                  0                 0
  [78,]                 0                   0                  1                 0
  [79,]                 0                   1                  0                 0
  [80,]                 0                   1                  0                 0
  [81,]                 0                   0                  1                 0
  [82,]                 0                   0                  1                 0
  [83,]                 0                   0                  1                 0
  [84,]                 0                   1                  0                 0
  [85,]                 0                   1                  0                 0
  [86,]                 0                   0                  1                 0
  [87,]                 0                   1                  0                 0
  [88,]                 1                   0                  0                 0
  [89,]                 0                   1                  0                 0
  [90,]                 0                   1                  0                 0
  [91,]                 0                   0                  1                 0
  [92,]                 0                   0                  1                 0
  [93,]                 0                   1                  0                 0
  [94,]                 0                   1                  0                 0
  [95,]                 0                   0                  0                 1
  [96,]                 0                   0                  1                 0
  [97,]                 0                   1                  0                 0
  [98,]                 0                   1                  0                 0
  [99,]                 0                   1                  0                 0
 [100,]                 0                   0                  1                 0
 [101,]                 0                   0                  1                 0
 [102,]                 0                   1                  0                 0
 [103,]                 0                   1                  0                 0
 [104,]                 0                   0                  1                 0
 [105,]                 0                   0                  1                 0
 [106,]                 0                   1                  0                 0
 [107,]                 0                   1                  0                 0
 [108,]                 1                   0                  0                 0
 [109,]                 0                   1                  0                 0
 [110,]                 0                   0                  1                 0
 [111,]                 0                   0                  1                 0
 [112,]                 0                   1                  0                 0
 [113,]                 0                   1                  0                 0
 [114,]                 0                   0                  1                 0
 [115,]                 0                   1                  0                 0
 [116,]                 1                   0                  0                 0
 [117,]                 0                   0                  1                 0
 [118,]                 0                   1                  0                 0
 [119,]                 0                   1                  0                 0
 [120,]                 0                   0                  1                 0
 [121,]                 0                   0                  1                 0
 [122,]                 0                   1                  0                 0
 [123,]                 0                   1                  0                 0
 [124,]                 0                   0                  1                 0
 [125,]                 0                   0                  0                 1
 [126,]                 0                   1                  0                 0
 [127,]                 0                   1                  0                 0
 [128,]                 0                   1                  0                 0
 [129,]                 0                   0                  1                 0
 [130,]                 1                   0                  0                 0
 [131,]                 0                   0                  1                 0
 [132,]                 0                   1                  0                 0
 [133,]                 0                   1                  0                 0
 [134,]                 0                   0                  1                 0
 [135,]                 0                   0                  1                 0
 [136,]                 0                   1                  0                 0
 [137,]                 0                   1                  0                 0
 [138,]                 0                   1                  0                 0
 [139,]                 0                   0                  1                 0
 [140,]                 0                   1                  0                 0
 [141,]                 0                   1                  0                 0
 [142,]                 0                   1                  0                 0
 [143,]                 0                   0                  1                 0
 [144,]                 0                   0                  1                 0
 [145,]                 0                   0                  1                 0
 [146,]                 1                   0                  0                 0
 [147,]                 1                   0                  0                 0
 [148,]                 0                   1                  0                 0
 [149,]                 0                   1                  0                 0
 [150,]                 0                   0                  1                 0
 [151,]                 0                   0                  0                 1
 [152,]                 0                   1                  0                 0
 [153,]                 0                   0                  1                 0
 [154,]                 0                   0                  1                 0
 [155,]                 0                   0                  1                 0
 [156,]                 0                   0                  1                 0
 [157,]                 0                   1                  0                 0
 [158,]                 0                   0                  1                 0
 [159,]                 0                   0                  1                 0
 [160,]                 0                   1                  0                 0
 [161,]                 0                   0                  1                 0
 [162,]                 0                   0                  0                 1
 [163,]                 0                   0                  1                 0
 [164,]                 0                   1                  0                 0
 [165,]                 0                   1                  0                 0
 [166,]                 0                   0                  1                 0
 [167,]                 0                   1                  0                 0
 [168,]                 0                   0                  1                 0
 [169,]                 0                   1                  0                 0
 [170,]                 0                   1                  0                 0
 [171,]                 0                   1                  0                 0
 [172,]                 1                   0                  0                 0
 [173,]                 0                   0                  1                 0
 [174,]                 0                   1                  0                 0
 [175,]                 0                   1                  0                 0
 [176,]                 0                   1                  0                 0
 [177,]                 0                   0                  1                 0
 [178,]                 0                   1                  0                 0
 [179,]                 0                   1                  0                 0
 [180,]                 0                   1                  0                 0
 [181,]                 0                   0                  1                 0
 [182,]                 0                   0                  1                 0
 [183,]                 0                   0                  1                 0
 [184,]                 0                   1                  0                 0
 [185,]                 0                   1                  0                 0
 [186,]                 0                   1                  0                 0
 [187,]                 0                   1                  0                 0
 [188,]                 0                   0                  1                 0
 [189,]                 0                   0                  1                 0
 [190,]                 0                   0                  1                 0
 [191,]                 0                   1                  0                 0
 [192,]                 0                   0                  1                 0
 [193,]                 0                   0                  1                 0
 [194,]                 0                   0                  1                 0
 [195,]                 0                   1                  0                 0
 [196,]                 0                   1                  0                 0
 [197,]                 0                   0                  0                 1
 [198,]                 1                   0                  0                 0
 [199,]                 0                   1                  0                 0
 [200,]                 0                   0                  1                 0
 [201,]                 1                   0                  0                 0
 [202,]                 0                   0                  1                 0
 [203,]                 0                   1                  0                 0
 [204,]                 0                   0                  1                 0
 [205,]                 0                   0                  1                 0
 [206,]                 0                   0                  1                 0
 [207,]                 0                   0                  1                 0
 [208,]                 0                   0                  0                 1
 [209,]                 0                   0                  1                 0
 [210,]                 0                   1                  0                 0
 [211,]                 0                   0                  0                 1
 [212,]                 0                   1                  0                 0
 [213,]                 0                   1                  0                 0
 [214,]                 0                   0                  1                 0
 [215,]                 0                   0                  1                 0
 [216,]                 0                   0                  1                 0
 [217,]                 0                   0                  1                 0
 [218,]                 0                   1                  0                 0
 [219,]                 0                   0                  1                 0
 [220,]                 0                   1                  0                 0
 [221,]                 0                   1                  0                 0
 [222,]                 1                   0                  0                 0
 [223,]                 0                   1                  0                 0
 [224,]                 0                   1                  0                 0
 [225,]                 0                   1                  0                 0
 [226,]                 0                   1                  0                 0
 [227,]                 0                   1                  0                 0
 [228,]                 0                   1                  0                 0
 [229,]                 0                   0                  1                 0
 [230,]                 0                   1                  0                 0
 [231,]                 0                   0                  1                 0
 [232,]                 0                   0                  1                 0
 [233,]                 0                   0                  1                 0
 [234,]                 0                   0                  0                 1
 [235,]                 0                   0                  1                 0
 [236,]                 0                   0                  1                 0
 [237,]                 1                   0                  0                 0
 [238,]                 0                   1                  0                 0
 [239,]                 0                   0                  1                 0
 [240,]                 0                   0                  1                 0
 [241,]                 0                   0                  1                 0
 [242,]                 0                   0                  1                 0
 [243,]                 0                   1                  0                 0
 [244,]                 0                   0                  1                 0
 [245,]                 0                   0                  1                 0
 [246,]                 0                   1                  0                 0
 [247,]                 1                   0                  0                 0
 [248,]                 0                   1                  0                 0
 [249,]                 0                   1                  0                 0
 [250,]                 0                   1                  0                 0
 [ reached getOption("max.print") -- omitted 5038 rows ]
bank_data$education <- NULL
bank_data

Converting Contact variable into dummy variable

cbind(bank_data,dummy(bank_data$contact, sep = "_"))

education_dummy <- dummy(bank_data$contact, sep = "_")
education_dummy
        contact_cellular contact_telephone contact_unknown
   [1,]                1                 0               0
   [2,]                1                 0               0
   [3,]                1                 0               0
   [4,]                1                 0               0
   [5,]                1                 0               0
   [6,]                1                 0               0
   [7,]                1                 0               0
   [8,]                0                 1               0
   [9,]                1                 0               0
  [10,]                0                 0               1
  [11,]                1                 0               0
  [12,]                1                 0               0
  [13,]                1                 0               0
  [14,]                1                 0               0
  [15,]                1                 0               0
  [16,]                1                 0               0
  [17,]                1                 0               0
  [18,]                1                 0               0
  [19,]                1                 0               0
  [20,]                1                 0               0
  [21,]                1                 0               0
  [22,]                1                 0               0
  [23,]                1                 0               0
  [24,]                1                 0               0
  [25,]                1                 0               0
  [26,]                1                 0               0
  [27,]                0                 0               1
  [28,]                1                 0               0
  [29,]                1                 0               0
  [30,]                1                 0               0
  [31,]                1                 0               0
  [32,]                0                 1               0
  [33,]                1                 0               0
  [34,]                1                 0               0
  [35,]                1                 0               0
  [36,]                1                 0               0
  [37,]                0                 0               1
  [38,]                1                 0               0
  [39,]                1                 0               0
  [40,]                1                 0               0
  [41,]                0                 1               0
  [42,]                0                 1               0
  [43,]                1                 0               0
  [44,]                1                 0               0
  [45,]                1                 0               0
  [46,]                1                 0               0
  [47,]                0                 0               1
  [48,]                1                 0               0
  [49,]                1                 0               0
  [50,]                0                 1               0
  [51,]                1                 0               0
  [52,]                1                 0               0
  [53,]                1                 0               0
  [54,]                1                 0               0
  [55,]                0                 0               1
  [56,]                1                 0               0
  [57,]                1                 0               0
  [58,]                1                 0               0
  [59,]                1                 0               0
  [60,]                1                 0               0
  [61,]                0                 1               0
  [62,]                1                 0               0
  [63,]                1                 0               0
  [64,]                1                 0               0
  [65,]                1                 0               0
  [66,]                1                 0               0
  [67,]                1                 0               0
  [68,]                1                 0               0
  [69,]                0                 0               1
  [70,]                1                 0               0
  [71,]                1                 0               0
  [72,]                1                 0               0
  [73,]                1                 0               0
  [74,]                1                 0               0
  [75,]                1                 0               0
  [76,]                1                 0               0
  [77,]                1                 0               0
  [78,]                1                 0               0
  [79,]                1                 0               0
  [80,]                1                 0               0
  [81,]                1                 0               0
  [82,]                1                 0               0
  [83,]                1                 0               0
  [84,]                1                 0               0
  [85,]                1                 0               0
  [86,]                1                 0               0
  [87,]                1                 0               0
  [88,]                1                 0               0
  [89,]                1                 0               0
  [90,]                1                 0               0
  [91,]                1                 0               0
  [92,]                0                 0               1
  [93,]                1                 0               0
  [94,]                1                 0               0
  [95,]                1                 0               0
  [96,]                1                 0               0
  [97,]                1                 0               0
  [98,]                1                 0               0
  [99,]                1                 0               0
 [100,]                1                 0               0
 [101,]                1                 0               0
 [102,]                1                 0               0
 [103,]                1                 0               0
 [104,]                1                 0               0
 [105,]                1                 0               0
 [106,]                1                 0               0
 [107,]                1                 0               0
 [108,]                1                 0               0
 [109,]                1                 0               0
 [110,]                1                 0               0
 [111,]                1                 0               0
 [112,]                1                 0               0
 [113,]                1                 0               0
 [114,]                1                 0               0
 [115,]                1                 0               0
 [116,]                0                 0               1
 [117,]                1                 0               0
 [118,]                1                 0               0
 [119,]                1                 0               0
 [120,]                1                 0               0
 [121,]                1                 0               0
 [122,]                1                 0               0
 [123,]                1                 0               0
 [124,]                1                 0               0
 [125,]                1                 0               0
 [126,]                1                 0               0
 [127,]                1                 0               0
 [128,]                1                 0               0
 [129,]                0                 1               0
 [130,]                1                 0               0
 [131,]                1                 0               0
 [132,]                0                 0               1
 [133,]                0                 0               1
 [134,]                1                 0               0
 [135,]                1                 0               0
 [136,]                1                 0               0
 [137,]                1                 0               0
 [138,]                1                 0               0
 [139,]                1                 0               0
 [140,]                1                 0               0
 [141,]                1                 0               0
 [142,]                0                 1               0
 [143,]                1                 0               0
 [144,]                1                 0               0
 [145,]                1                 0               0
 [146,]                1                 0               0
 [147,]                1                 0               0
 [148,]                1                 0               0
 [149,]                1                 0               0
 [150,]                1                 0               0
 [151,]                1                 0               0
 [152,]                1                 0               0
 [153,]                1                 0               0
 [154,]                1                 0               0
 [155,]                1                 0               0
 [156,]                1                 0               0
 [157,]                1                 0               0
 [158,]                0                 0               1
 [159,]                1                 0               0
 [160,]                1                 0               0
 [161,]                1                 0               0
 [162,]                1                 0               0
 [163,]                1                 0               0
 [164,]                1                 0               0
 [165,]                1                 0               0
 [166,]                1                 0               0
 [167,]                1                 0               0
 [168,]                1                 0               0
 [169,]                1                 0               0
 [170,]                1                 0               0
 [171,]                1                 0               0
 [172,]                1                 0               0
 [173,]                1                 0               0
 [174,]                1                 0               0
 [175,]                1                 0               0
 [176,]                1                 0               0
 [177,]                1                 0               0
 [178,]                1                 0               0
 [179,]                1                 0               0
 [180,]                1                 0               0
 [181,]                1                 0               0
 [182,]                1                 0               0
 [183,]                1                 0               0
 [184,]                1                 0               0
 [185,]                1                 0               0
 [186,]                1                 0               0
 [187,]                1                 0               0
 [188,]                1                 0               0
 [189,]                1                 0               0
 [190,]                1                 0               0
 [191,]                1                 0               0
 [192,]                1                 0               0
 [193,]                0                 1               0
 [194,]                1                 0               0
 [195,]                1                 0               0
 [196,]                1                 0               0
 [197,]                1                 0               0
 [198,]                1                 0               0
 [199,]                1                 0               0
 [200,]                1                 0               0
 [201,]                1                 0               0
 [202,]                1                 0               0
 [203,]                1                 0               0
 [204,]                1                 0               0
 [205,]                1                 0               0
 [206,]                1                 0               0
 [207,]                1                 0               0
 [208,]                1                 0               0
 [209,]                1                 0               0
 [210,]                1                 0               0
 [211,]                1                 0               0
 [212,]                1                 0               0
 [213,]                0                 1               0
 [214,]                1                 0               0
 [215,]                1                 0               0
 [216,]                1                 0               0
 [217,]                1                 0               0
 [218,]                1                 0               0
 [219,]                1                 0               0
 [220,]                1                 0               0
 [221,]                1                 0               0
 [222,]                1                 0               0
 [223,]                0                 1               0
 [224,]                0                 1               0
 [225,]                1                 0               0
 [226,]                1                 0               0
 [227,]                1                 0               0
 [228,]                0                 0               1
 [229,]                1                 0               0
 [230,]                0                 0               1
 [231,]                1                 0               0
 [232,]                1                 0               0
 [233,]                1                 0               0
 [234,]                1                 0               0
 [235,]                1                 0               0
 [236,]                1                 0               0
 [237,]                1                 0               0
 [238,]                1                 0               0
 [239,]                1                 0               0
 [240,]                1                 0               0
 [241,]                1                 0               0
 [242,]                1                 0               0
 [243,]                0                 0               1
 [244,]                1                 0               0
 [245,]                1                 0               0
 [246,]                1                 0               0
 [247,]                1                 0               0
 [248,]                1                 0               0
 [249,]                1                 0               0
 [250,]                1                 0               0
 [251,]                1                 0               0
 [252,]                1                 0               0
 [253,]                1                 0               0
 [254,]                1                 0               0
 [255,]                1                 0               0
 [256,]                1                 0               0
 [257,]                1                 0               0
 [258,]                1                 0               0
 [259,]                1                 0               0
 [260,]                1                 0               0
 [261,]                1                 0               0
 [262,]                1                 0               0
 [263,]                1                 0               0
 [264,]                0                 1               0
 [265,]                0                 1               0
 [266,]                1                 0               0
 [267,]                0                 0               1
 [268,]                1                 0               0
 [269,]                1                 0               0
 [270,]                1                 0               0
 [271,]                0                 0               1
 [272,]                1                 0               0
 [273,]                1                 0               0
 [274,]                1                 0               0
 [275,]                0                 1               0
 [276,]                1                 0               0
 [277,]                1                 0               0
 [278,]                1                 0               0
 [279,]                1                 0               0
 [280,]                1                 0               0
 [281,]                0                 0               1
 [282,]                1                 0               0
 [283,]                1                 0               0
 [284,]                1                 0               0
 [285,]                0                 0               1
 [286,]                1                 0               0
 [287,]                1                 0               0
 [288,]                1                 0               0
 [289,]                1                 0               0
 [290,]                1                 0               0
 [291,]                0                 1               0
 [292,]                0                 1               0
 [293,]                1                 0               0
 [294,]                1                 0               0
 [295,]                1                 0               0
 [296,]                1                 0               0
 [297,]                1                 0               0
 [298,]                1                 0               0
 [299,]                0                 0               1
 [300,]                1                 0               0
 [301,]                1                 0               0
 [302,]                1                 0               0
 [303,]                1                 0               0
 [304,]                1                 0               0
 [305,]                1                 0               0
 [306,]                1                 0               0
 [307,]                1                 0               0
 [308,]                1                 0               0
 [309,]                0                 0               1
 [310,]                1                 0               0
 [311,]                1                 0               0
 [312,]                1                 0               0
 [313,]                1                 0               0
 [314,]                0                 1               0
 [315,]                1                 0               0
 [316,]                1                 0               0
 [317,]                0                 0               1
 [318,]                1                 0               0
 [319,]                0                 1               0
 [320,]                0                 1               0
 [321,]                1                 0               0
 [322,]                1                 0               0
 [323,]                1                 0               0
 [324,]                1                 0               0
 [325,]                1                 0               0
 [326,]                0                 1               0
 [327,]                1                 0               0
 [328,]                1                 0               0
 [329,]                1                 0               0
 [330,]                0                 1               0
 [331,]                1                 0               0
 [332,]                1                 0               0
 [333,]                1                 0               0
 [ reached getOption("max.print") -- omitted 4955 rows ]
bank_data$contact <- NULL
bank_data

Converting Month variable into dummy variable

cbind(bank_data,dummy(bank_data$month, sep = "_"))

month_dummy <- dummy(bank_data$month, sep = "_")
month_dummy
        month_apr month_aug month_dec month_feb month_jan month_jul month_jun month_mar
   [1,]         0         0         0         0         1         0         0         0
   [2,]         1         0         0         0         0         0         0         0
   [3,]         0         0         0         0         0         0         1         0
   [4,]         0         0         0         0         0         1         0         0
   [5,]         0         1         0         0         0         0         0         0
   [6,]         0         0         0         0         0         1         0         0
   [7,]         0         1         0         0         0         0         0         0
   [8,]         0         0         0         0         0         0         0         0
   [9,]         0         1         0         0         0         0         0         0
  [10,]         0         0         0         0         0         0         1         0
  [11,]         0         1         0         0         0         0         0         0
  [12,]         0         1         0         0         0         0         0         0
  [13,]         0         0         0         0         0         0         0         0
  [14,]         1         0         0         0         0         0         0         0
  [15,]         0         0         0         1         0         0         0         0
  [16,]         0         0         0         0         0         0         0         0
  [17,]         0         0         0         0         0         0         0         0
  [18,]         0         0         0         0         0         0         0         0
  [19,]         1         0         0         0         0         0         0         0
  [20,]         0         0         0         1         0         0         0         0
  [21,]         0         1         0         0         0         0         0         0
  [22,]         0         1         0         0         0         0         0         0
  [23,]         0         0         0         0         0         0         0         0
  [24,]         0         0         0         0         0         1         0         0
  [25,]         0         0         0         0         1         0         0         0
  [26,]         1         0         0         0         0         0         0         0
  [27,]         0         0         0         0         0         0         0         0
  [28,]         1         0         0         0         0         0         0         0
  [29,]         0         0         0         0         0         0         0         0
  [30,]         0         0         0         0         0         0         0         1
  [31,]         0         0         0         0         0         0         0         0
  [32,]         0         1         0         0         0         0         0         0
  [33,]         0         0         0         0         0         0         0         0
  [34,]         0         1         0         0         0         0         0         0
  [35,]         0         0         0         0         0         0         0         1
  [36,]         0         0         0         0         0         0         0         0
  [37,]         0         0         0         1         0         0         0         0
  [38,]         0         0         0         0         0         1         0         0
  [39,]         0         0         0         0         0         0         0         0
  [40,]         1         0         0         0         0         0         0         0
  [41,]         0         0         0         0         0         0         0         1
  [42,]         0         0         0         0         0         0         0         0
  [43,]         0         0         0         1         0         0         0         0
  [44,]         0         0         0         0         0         0         1         0
  [45,]         0         0         0         0         0         0         0         0
  [46,]         0         0         0         1         0         0         0         0
  [47,]         0         0         0         0         0         0         0         0
  [48,]         0         0         0         0         0         0         0         0
  [49,]         0         0         1         0         0         0         0         0
  [50,]         0         0         0         0         0         0         0         0
  [51,]         0         0         0         0         0         0         0         0
  [52,]         0         0         0         0         0         0         0         0
  [53,]         0         0         0         0         0         0         0         0
  [54,]         0         0         0         0         0         0         0         0
  [55,]         0         0         0         0         0         0         1         0
  [56,]         0         0         0         0         0         1         0         0
  [57,]         0         0         0         1         0         0         0         0
  [58,]         0         0         0         0         0         0         0         0
  [59,]         0         0         0         0         0         0         0         0
  [60,]         1         0         0         0         0         0         0         0
  [61,]         0         0         0         0         0         1         0         0
  [62,]         0         0         0         1         0         0         0         0
  [63,]         0         0         0         0         0         0         0         0
  [64,]         0         0         0         0         0         0         0         1
  [65,]         0         0         0         0         0         0         0         0
  [66,]         0         0         0         0         0         0         0         0
  [67,]         0         0         0         0         0         0         0         0
  [68,]         0         0         0         0         1         0         0         0
  [69,]         0         0         0         0         0         0         1         0
  [70,]         0         0         0         0         0         0         0         0
  [71,]         0         0         0         0         0         0         0         1
  [72,]         1         0         0         0         0         0         0         0
  [73,]         0         0         0         0         0         0         0         1
  [74,]         0         0         0         0         0         0         0         1
  [75,]         0         0         0         0         0         0         0         0
  [76,]         0         0         0         0         0         0         1         0
  [77,]         0         0         0         0         0         0         0         0
  [78,]         0         0         0         0         0         0         0         0
  [79,]         0         0         0         1         0         0         0         0
  [80,]         0         1         0         0         0         0         0         0
  [81,]         1         0         0         0         0         0         0         0
  [82,]         0         0         0         0         0         0         0         0
  [83,]         1         0         0         0         0         0         0         0
        month_may month_nov month_oct month_sep
   [1,]         0         0         0         0
   [2,]         0         0         0         0
   [3,]         0         0         0         0
   [4,]         0         0         0         0
   [5,]         0         0         0         0
   [6,]         0         0         0         0
   [7,]         0         0         0         0
   [8,]         0         0         0         1
   [9,]         0         0         0         0
  [10,]         0         0         0         0
  [11,]         0         0         0         0
  [12,]         0         0         0         0
  [13,]         0         0         1         0
  [14,]         0         0         0         0
  [15,]         0         0         0         0
  [16,]         1         0         0         0
  [17,]         0         1         0         0
  [18,]         0         0         1         0
  [19,]         0         0         0         0
  [20,]         0         0         0         0
  [21,]         0         0         0         0
  [22,]         0         0         0         0
  [23,]         0         0         0         1
  [24,]         0         0         0         0
  [25,]         0         0         0         0
  [26,]         0         0         0         0
  [27,]         1         0         0         0
  [28,]         0         0         0         0
  [29,]         1         0         0         0
  [30,]         0         0         0         0
  [31,]         1         0         0         0
  [32,]         0         0         0         0
  [33,]         1         0         0         0
  [34,]         0         0         0         0
  [35,]         0         0         0         0
  [36,]         0         0         1         0
  [37,]         0         0         0         0
  [38,]         0         0         0         0
  [39,]         0         1         0         0
  [40,]         0         0         0         0
  [41,]         0         0         0         0
  [42,]         0         0         0         1
  [43,]         0         0         0         0
  [44,]         0         0         0         0
  [45,]         1         0         0         0
  [46,]         0         0         0         0
  [47,]         1         0         0         0
  [48,]         1         0         0         0
  [49,]         0         0         0         0
  [50,]         0         0         1         0
  [51,]         1         0         0         0
  [52,]         1         0         0         0
  [53,]         1         0         0         0
  [54,]         0         1         0         0
  [55,]         0         0         0         0
  [56,]         0         0         0         0
  [57,]         0         0         0         0
  [58,]         1         0         0         0
  [59,]         0         0         0         1
  [60,]         0         0         0         0
  [61,]         0         0         0         0
  [62,]         0         0         0         0
  [63,]         1         0         0         0
  [64,]         0         0         0         0
  [65,]         0         0         0         1
  [66,]         0         1         0         0
  [67,]         1         0         0         0
  [68,]         0         0         0         0
  [69,]         0         0         0         0
  [70,]         1         0         0         0
  [71,]         0         0         0         0
  [72,]         0         0         0         0
  [73,]         0         0         0         0
  [74,]         0         0         0         0
  [75,]         1         0         0         0
  [76,]         0         0         0         0
  [77,]         1         0         0         0
  [78,]         0         1         0         0
  [79,]         0         0         0         0
  [80,]         0         0         0         0
  [81,]         0         0         0         0
  [82,]         0         1         0         0
  [83,]         0         0         0         0
 [ reached getOption("max.print") -- omitted 5205 rows ]
bank_data$month <- NULL
bank_data

Converting poutcome variable into dummy variable

Converting default categorical variable into numerical

bank_data$default <- as.numeric(as.character(factor(bank_data$default,levels=c('yes','no'),
                    labels =c(1,0) )))
bank_data$default
   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  [45] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  [89] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [133] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [221] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [309] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [353] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [485] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [529] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [573] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [617] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [661] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [705] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [749] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [837] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [881] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [925] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [ reached getOption("max.print") -- omitted 4288 entries ]

Converting housing categorical variable into numerical

bank_data$housing <- as.numeric(as.character(factor(bank_data$housing,levels=c('yes','no'),
                    labels =c(1,0) )))
bank_data$housing
   [1] 0 1 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1
  [45] 0 1 1 1 0 0 1 0 1 0 1 1 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 1 1
  [89] 1 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 1 0 0 0 1 0 1 0 1 1 0 0 0 1 0
 [133] 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0
 [177] 0 0 0 1 1 0 1 1 1 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0 1
 [221] 1 1 0 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 0
 [265] 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 1 0 0 1
 [309] 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1
 [353] 0 0 1 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1
 [397] 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0
 [441] 1 0 1 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 0
 [485] 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
 [529] 1 0 0 0 0 0 1 1 0 1 1 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0
 [573] 1 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1
 [617] 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0
 [661] 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0
 [705] 0 1 1 1 1 0 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 1 0 1 0 0 1 1 1 1 1 1 1 1 0 0 0 0
 [749] 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1
 [793] 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0
 [837] 1 0 0 1 1 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0
 [881] 1 1 0 0 1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0 0 1 1 1 1 1 0 0 0 1 0 1
 [925] 0 0 0 1 1 1 1 0 0 1 1 1 0 1 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1 0 1 1 1 0 0 1
 [969] 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 1
 [ reached getOption("max.print") -- omitted 4288 entries ]

Converting loan categorical variable into numerical

bank_data$loan <- as.numeric(as.character(factor(bank_data$loan,levels = c('yes','no'),
                    labels =c(1,0) )))
bank_data$loan
   [1] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
  [45] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
  [89] 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [133] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [177] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0
 [221] 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0
 [265] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1
 [309] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0
 [353] 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0
 [397] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [441] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [485] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0
 [529] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
 [573] 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
 [617] 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [661] 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
 [705] 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [749] 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
 [793] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [837] 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
 [881] 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [925] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
 [969] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0
 [ reached getOption("max.print") -- omitted 4288 entries ]

Converting y categorical variable into numerical

#bank_data$y <- as.numeric(as.character(factor(bank_data$y,levels = c('yes','no'),
#                    labels =c(1,0))))
#bank_data$y
View(bank_data)

Creating New Dataset with the required Columns

bank_dataset <- cbind(job_dummy,marital_dummy,education_dummy,month_dummy,pouctome_dummy,(bank_data))
View(bank_dataset)

Normalising the data

normalize_data <- bank_dataset
norm.values <- preProcess(normalize_data[,1:41], method = c("center", "scale"))
normalize_data[,1:41] <- predict(norm.values, normalize_data[,1:44])
Error in `[.data.frame`(normalize_data, , 1:44) : 
  undefined columns selected

Dividing Data into Training Set, Validation Set and Test Set

set.seed(1)
idx <- sample(seq(1, 3), size = nrow(normalize_data), replace = TRUE, prob = c(.7, .2, .1))
train.df <- normalize_data[idx == 1,]
valid.df <- normalize_data[idx == 2,]
test.df <- normalize_data[idx == 3,]
nrow(train.df)
[1] 3675
nrow(valid.df)
[1] 1052
nrow(test.df)
[1] 561

Point Biserial Correlation to find relation between continuous and categorical data

install.packages(“ltm”)

biserial.cor(bank.full$age, bank.full$y, use = c("all.obs", "complete.obs"), level = 1)
[1] -0.02515502

Finding Correlations Among Continuous Variable

cor_data <- train.df[c('age','balance','day','duration','campaign','pdays','previous')]
cor(cor_data)
                   age      balance          day      duration     campaign        pdays
age       1.0000000000  0.112284427 -0.009183551  0.0005928877 -0.008332416  0.007017854
balance   0.1122844272  1.000000000 -0.014891864  0.0072597918 -0.009986158  0.017135883
day      -0.0091835506 -0.014891864  1.000000000 -0.0176419511  0.118191369 -0.063199263
duration  0.0005928877  0.007259792 -0.017641951  1.0000000000 -0.022193163 -0.058779389
campaign -0.0083324157 -0.009986158  0.118191369 -0.0221931628  1.000000000 -0.095514860
pdays     0.0070178536  0.017135883 -0.063199263 -0.0587793889 -0.095514860  1.000000000
previous  0.0253065448  0.046640417 -0.048067064 -0.0295119004 -0.034617033  0.501037123
            previous
age       0.02530654
balance   0.04664042
day      -0.04806706
duration -0.02951190
campaign -0.03461703
pdays     0.50103712
previous  1.00000000

Boxplots (Comparing the Predictors with the Output Variable)

par(mfrow=c(2,2))
boxplot(train.df$age ~ train.df$y,main="Age", col=c('powderblue', 'mistyrose'))
boxplot(train.df$balance ~ train.df$y,main="Balance", col=c('powderblue', 'mistyrose'))
boxplot(train.df$day ~ train.df$y,main="day", col=c('powderblue', 'mistyrose'))
boxplot(train.df$duration ~ train.df$y,main="duration", col=c('powderblue', 'mistyrose'))

boxplot(train.df$campaign ~ train.df$y,main="campaign", col=c('powderblue', 'mistyrose'))
boxplot(train.df$pdays ~ train.df$y,main="pdays", col=c('powderblue', 'mistyrose'))
boxplot(train.df$previous ~ train.df$y,main="previous", col=c('powderblue', 'mistyrose'))

Interpretation for Box Plots

  1. Longer Duration of calls results in that person will deposit in the bank
  2. Pdays i.e. person contacted in the past also contibutes to outcome variable
  3. People contacted more leads to person deposition in the bank
  4. Other predictors including Age and Balance very much impacts the outcome variable in both i.e. yes and no

Function for Multiplot to plot multiple graphs together

multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
  library(grid)
  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)
  numPlots = length(plots)
  # If layout is NULL, then use 'cols' to determine layout
  if (is.null(layout)) {
    # Make the panel
    # ncol: Number of columns of plots
    # nrow: Number of rows needed, calculated from # of cols
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                    ncol = cols, nrow = ceiling(numPlots/cols))
  }
 if (numPlots==1) {
    print(plots[[1]])
  } else {
    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
    # Make each plot, in the correct location
    for (i in 1:numPlots) {
      # Get the i,j matrix positions of the regions that contain this subplot
      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                      layout.pos.col = matchidx$col))
    }
  }
}

GGPLOT for Marital with the other predictors

marital_duration<-summarise(group_by(bank.full,marital),duration=mean(duration))
marital_duration
p1<-ggplot(marital_duration,aes(x=marital,y=duration,fill=marital))+
geom_bar(stat='identity')
marital_balance<-summarise(group_by(bank.full,marital),balance=mean(balance))
marital_balance
p2<-ggplot(marital_balance,aes(x=marital,y=balance,fill=marital))+ geom_bar(stat='identity')
marital_age<-summarise(group_by(bank.full,marital),age=mean(age))
marital_age
p3<-ggplot(marital_age,aes(x=marital,y=age,fill=marital))+ geom_bar(stat='identity')
marital_pdays<-summarise(group_by(bank.full,marital),pdays=mean(pdays))
marital_pdays
p4<-ggplot(marital_pdays,aes(x=marital,y=pdays,fill=marital))+ geom_bar(stat='identity')
multiplot(p1, p2, p3, p4, cols=2)

GGPLOT for Job Variable with the other predictors

job_duration<-summarise(group_by(bank.full,job),duration=mean(duration))
job_duration
p1<-ggplot(job_duration,aes(x=job,y=duration,fill=job))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 0.5))
job_balance<-summarise(group_by(bank.full,job),balance=mean(balance))
job_balance
p2<-ggplot(job_balance,aes(x=job,y=balance,fill=job))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 0.5))
job_age<-summarise(group_by(bank.full,job),age=mean(age))
job_age
p3<-ggplot(job_age,aes(x=job,y=age,fill=job))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 0.5))
job_pdays<-summarise(group_by(bank.full,job),pdays=mean(pdays))
job_pdays
p4<-ggplot(job_pdays,aes(x=job,y=pdays,fill=job))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 0.5))
multiplot(p1, p2, p3, p4, cols=2)

GGPLOT for Education with the other predictors

education_duration<-summarise(group_by(bank.full,education),duration=mean(duration))
education_duration
p1<-ggplot(education_duration,aes(x=education,y=duration,fill=education))+
geom_bar(stat='identity')
education_balance<-summarise(group_by(bank.full,education),balance=mean(balance))
education_balance
p2<-ggplot(education_balance,aes(x=education,y=balance,fill=education))+
geom_bar(stat='identity')
education_age<-summarise(group_by(bank.full,education),age=mean(age))
education_age
p3<-ggplot(education_age,aes(x=education,y=age,fill=education))+ geom_bar(stat='identity')
education_pdays<-summarise(group_by(bank.full,education),age=mean(pdays))
education_pdays
p4<-ggplot(education_pdays,aes(x=education,y=age,fill=education))+ geom_bar(stat='identity')
multiplot(p1, p2, p3, p4, cols=2)

GGPLOT for outcome Y with the other predictors

ggplot(bank.full,aes(x=education,fill=education))+ geom_bar(stat='count',aes(fill =
factor(y)),position = position_dodge(width = 0.9))

ggplot(bank.full,aes(x=marital,fill=marital))+ geom_bar(stat='count',aes(fill =
factor(y)),position = position_dodge(width = 0.9))

ggplot(bank.full,aes(x=job,fill=job))+ geom_bar(stat='count',aes(fill =
factor(y)),position = position_dodge(width = 0.9))+theme(axis.text.x =
element_text(angle = 45, hjust = 1, vjust = 0.5))

ggplot(bank.full,aes(x=contact,fill=contact))+ geom_bar(stat='count',aes(fill =
factor(y)),position = position_dodge(width = 0.9))

Contact vs Age Predictor

ggplot(bank.full,aes(x=contact,fill=contact))+ geom_bar(stat='count',aes(fill =
factor(age)),position = position_dodge(width = 0.9))

Scatter Plot Between Age and other Continuous Variables

par(mfrow=c(2,2))
plot(log(train.df$age), log(train.df$balance), main = "Age Vs Balance", xlab = "Age", ylab = "Balance", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$balance) ~ log(train.df$age)))
NaNs producedNaNs produced
plot(log(train.df$age), log(train.df$duration), main = "Age Vs Duration", xlab = "Age", ylab = "Duration", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$duration) ~ log(train.df$age)))
NaNs producedNaNs produced
plot(log(train.df$age), log(train.df$pdays), main = "Age Vs Days Past", xlab = "Age", ylab = "Days Past", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$pdays) ~ log(train.df$age)))
NaNs producedNaNs produced
plot(log(train.df$age), log(train.df$previous), main = "Age Vs Previously Contacted", xlab = "Age", ylab = "Previously Contacted", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$previous) ~ log(train.df$age)))
NaNs producedNaNs produced

plot(log(train.df$age), log(train.df$day), main = "Age Vs Day", xlab = "Age", ylab = "Day", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$day) ~ log(train.df$age)))
NaNs producedNaNs produced
plot(log(train.df$age), log(train.df$campaign), main = "Age Vs Campaign", xlab = "Age", ylab = "Campaign", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$campaign) ~ log(train.df$age)))
NaNs producedNaNs produced

Scatter Plot Between Balance and other Continuous Variables

par(mfrow=c(2,2))
plot(log(train.df$balance), log(train.df$duration), main = "Duration Vs Balance", xlab = "Balance", ylab = "Duration", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$duration) ~ log(train.df$balance)))
NaNs producedNaNs produced
plot(log(train.df$duration), log(train.df$pdays), main = "Duration Vs Days Past", xlab = "Duration", ylab = "Days Past", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$pdays) ~ log(train.df$duration)))
NaNs producedNaNs produced
plot(log(train.df$duration), log(train.df$previous), main = "Duration Vs Previously Contacted", xlab = "Duration", ylab = "Previously Contacted", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$previous) ~ log(train.df$duration)))
NaNs producedNaNs produced
plot(log(train.df$duration), log(train.df$day), main = "Duration Vs Day", xlab = "Duration", ylab = "Day", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$day) ~ log(train.df$duration)))
NaNs producedNaNs produced

plot(log(train.df$duration), log(train.df$campaign), main = "Duration Vs Campaign", xlab = "Duration", ylab = "Campaign", col = 2)
NaNs producedNaNs produced
abline(lm(log(train.df$campaign) ~ log(train.df$duration)))
NaNs producedNaNs produced

Interpretation for Scatter Plots

  1. Balance have a positive relation with the Age
  2. Days, Duration, Pdays, Previous, Day and Campaign have a negative or no relation with the Age predictor
  3. Campagin has positive relation with the Duration
  4. Balance have negative relation with the Duration
  5. Pdays, Previous and Day have no relation with the Duration predictor

Principal Component Analysis on Continuous Predictors

install.packages(“readxl”) install.packages(‘psych’)

fa.parallel(normal_data, fm="pa", main = "Scree Plot With Parallel Analysis")
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 

pc <- principal(r = normal_data, nfactor = 3, rotate = "none")
pc
Principal Components Analysis
Call: principal(r = normal_data, nfactors = 3, rotate = "none")
Standardized loadings (pattern matrix) based upon correlation matrix

                       PC1  PC2  PC3
SS loadings           1.55 1.12 1.10
Proportion Var        0.22 0.16 0.16
Cumulative Var        0.22 0.38 0.54
Proportion Explained  0.41 0.30 0.29
Cumulative Proportion 0.41 0.71 1.00

Mean item complexity =  1.8
Test of the hypothesis that 3 components are sufficient.

The root mean square of the residuals (RMSR) is  0.16 
 with the empirical chi square  3852.01  with prob <  0 

Fit based upon off diagonal values = -0.73

Performing Rotation

pc_rotate
Principal Components Analysis
Call: principal(r = cont_data, nfactors = 3, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix

                       RC1  RC2  RC3
SS loadings           1.47 1.20 1.10
Proportion Var        0.21 0.17 0.16
Cumulative Var        0.21 0.38 0.54
Proportion Explained  0.39 0.32 0.29
Cumulative Proportion 0.39 0.71 1.00

Mean item complexity =  1
Test of the hypothesis that 3 components are sufficient.

The root mean square of the residuals (RMSR) is  0.16 
 with the empirical chi square  48046.71  with prob <  0 

Fit based upon off diagonal values = -0.95
pc_score <- principal(normal_data, nfactor = 3, scores = TRUE)
head(pc_score$scores)
             RC1        RC3        RC2
27766 -0.8019654 -0.3078275 -0.8798434
32554 -0.5098935 -0.5152472 -0.6841798
40346  1.8285781  0.1695993 -0.3515448
22128 -0.5807649  0.4997158  0.6121206
41611  0.3263004 -0.1517610  2.1938552
41205  0.4302717  0.4789325 -0.5068165

Interpretation

  1. Parallel Analysis suggests that Number of components to extract should be equal to 3 since 3 variables have eigenvalue greater than 1.
  2. Together all the components accounts to about 40 percent of the cumulative Variance both before and after rotating the components.
  3. There is no such major changes in the Proportion and Cumulative variance after rotating the components.
  4. The loading in RC1 indicates that first component is primarily defined by pdays and previous variables.
  5. While loading in RC2 indicates that second component is primarily defined by day and campaign

Applying Models

Applying Logistic Regression Model to select the relevant predictors (Dimension Reduction)

logit_model <- glm(y ~ .,data = train.df, family = binomial(link = "logit"))
summary(logit_model)

Call:
glm(formula = y ~ ., family = binomial(link = "logit"), data = train.df)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-5.1113  -0.5643   0.0026   0.6066   2.9976  

Coefficients: (4 not defined because of singularities)
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)          0.148997   0.048483   3.073  0.00212 ** 
job_admin.          -0.063900   0.176641  -0.362  0.71754    
`job_blue-collar`   -0.284535   0.210942  -1.349  0.17738    
job_entrepreneur    -0.050385   0.099942  -0.504  0.61416    
job_housemaid       -0.109636   0.096304  -1.138  0.25494    
job_management      -0.103918   0.225294  -0.461  0.64462    
job_retired          0.089117   0.147935   0.602  0.54690    
`job_self-employed` -0.099692   0.111503  -0.894  0.37128    
job_services        -0.188692   0.155252  -1.215  0.22422    
job_student          0.042809   0.107152   0.400  0.68951    
job_technician      -0.188238   0.202466  -0.930  0.35251    
job_unemployed      -0.092510   0.101577  -0.911  0.36243    
job_unknown                NA         NA      NA       NA    
marital_divorced     0.074644   0.055456   1.346  0.17830    
marital_married     -0.093141   0.059002  -1.579  0.11443    
marital_single             NA         NA      NA       NA    
contact_cellular     0.569848   0.078767   7.235 4.67e-13 ***
contact_telephone    0.258543   0.063717   4.058 4.96e-05 ***
contact_unknown            NA         NA      NA       NA    
month_apr           -0.197775   0.099804  -1.982  0.04752 *  
month_aug           -0.736551   0.120874  -6.094 1.10e-09 ***
month_dec           -0.075213   0.069968  -1.075  0.28239    
month_feb           -0.234373   0.093597  -2.504  0.01228 *  
month_jan           -0.382777   0.071551  -5.350 8.81e-08 ***
month_jul           -0.767311   0.125853  -6.097 1.08e-09 ***
month_jun           -0.370021   0.116247  -3.183  0.00146 ** 
month_mar            0.133735   0.074522   1.795  0.07272 .  
month_may           -0.874345   0.153207  -5.707 1.15e-08 ***
month_nov           -0.453234   0.097923  -4.628 3.68e-06 ***
month_oct            0.027795   0.082265   0.338  0.73546    
month_sep                  NA         NA      NA       NA    
age                 -0.118552   0.067598  -1.754  0.07947 .  
default             -0.008364   0.046032  -0.182  0.85581    
balance              0.050770   0.043568   1.165  0.24389    
housing             -0.427248   0.054924  -7.779 7.32e-15 ***
loan                -0.223600   0.049575  -4.510 6.47e-06 ***
day                  0.043336   0.052518   0.825  0.40927    
duration             2.297755   0.084221  27.282  < 2e-16 ***
campaign            -0.126758   0.056745  -2.234  0.02550 *  
pdays                0.028967   0.081983   0.353  0.72384    
previous             0.037422   0.057749   0.648  0.51698    
poutcome            -0.238581   0.085005  -2.807  0.00501 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 5094.5  on 3674  degrees of freedom
Residual deviance: 2913.4  on 3637  degrees of freedom
AIC: 2989.4

Number of Fisher Scoring iterations: 6
exp(coef(logit_model))
        (Intercept)          job_admin.   `job_blue-collar`    job_entrepreneur 
          1.1606695           0.9380984           0.7523638           0.9508632 
      job_housemaid      job_management         job_retired `job_self-employed` 
          0.8961602           0.9012989           1.0932085           0.9051162 
       job_services         job_student      job_technician      job_unemployed 
          0.8280418           1.0437386           0.8284172           0.9116401 
        job_unknown    marital_divorced     marital_married      marital_single 
                 NA           1.0775007           0.9110653                  NA 
   contact_cellular   contact_telephone     contact_unknown           month_apr 
          1.7679981           1.2950423                  NA           0.8205544 
          month_aug           month_dec           month_feb           month_jan 
          0.4787624           0.9275457           0.7910669           0.6819651 
          month_jul           month_jun           month_mar           month_may 
          0.4642599           0.6907195           1.1430893           0.4171354 
          month_nov           month_oct           month_sep                 age 
          0.6355692           1.0281852                  NA           0.8882053 
            default             balance             housing                loan 
          0.9916705           1.0520812           0.6523019           0.7996350 
                day            duration            campaign               pdays 
          1.0442892           9.9518131           0.8809472           1.0293906 
           previous            poutcome 
          1.0381311           0.7877449 

From the Logistic Regression Model, the following predictors have significant impact on the outcome i.e. either positive or negative:

  1. contact_cellular
  2. contact_telephone
  3. month_aug
  4. month_jan
  5. month_jul
  6. month_may
  7. month_nov
  8. poutcome
  9. housing
  10. loan
  11. duration
confusionMatrix(as.factor(result), valid.df$y)
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  478 197
       yes  54 323
                                          
               Accuracy : 0.7614          
                 95% CI : (0.7345, 0.7869)
    No Information Rate : 0.5057          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.5213          
 Mcnemar's Test P-Value : < 2.2e-16       
                                          
            Sensitivity : 0.8985          
            Specificity : 0.6212          
         Pos Pred Value : 0.7081          
         Neg Pred Value : 0.8568          
             Prevalence : 0.5057          
         Detection Rate : 0.4544          
   Detection Prevalence : 0.6416          
      Balanced Accuracy : 0.7598          
                                          
       'Positive' Class : no              
                                          

Based on Domain Knowldege the following selected predictors does not seem relevant predictors to train or make the model :

  1. contact_cellular
  2. contact_telephone
  3. month_aug
  4. month_jan
  5. month_jul
  6. month_may
  7. month_nov

There, we are left with the following predictors

  1. poutcome
  2. housing
  3. loan
  4. duration

Dataset with the required predictors

testing_data <- data.frame(train.df[,c(16,17,20,23,24,27,28,34,35,37,41,42)])
testing_data

testing_valid <- data.frame(valid.df[,c(16,17,20,23,24,27,28,34,35,37,41,42)])
testing_valid
ds_train <- data.frame(train.df[,c(34,35,37,41,42)])
ds_train
ds_valid <- data.frame(valid.df[,c(34,35,37,41,42)])
ds_valid
ds_test <- data.frame(test.df[,c(34,35,37,41,42)])
ds_test

Logistic Regression Model

With all the predictors given by Logistic Regression Model

confusionMatrix(as.factor(result), valid.df$y)
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  468 193
       yes  64 327
                                          
               Accuracy : 0.7557          
                 95% CI : (0.7286, 0.7814)
    No Information Rate : 0.5057          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.51            
 Mcnemar's Test P-Value : 1.412e-15       
                                          
            Sensitivity : 0.8797          
            Specificity : 0.6288          
         Pos Pred Value : 0.7080          
         Neg Pred Value : 0.8363          
             Prevalence : 0.5057          
         Detection Rate : 0.4449          
   Detection Prevalence : 0.6283          
      Balanced Accuracy : 0.7543          
                                          
       'Positive' Class : no              
                                          

Based on the predictors given by Logistic Regression Model and Domain Knowldege

confusionMatrix(as.factor(result), valid.df$y)
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  475 231
       yes  57 289
                                         
               Accuracy : 0.7262         
                 95% CI : (0.6982, 0.753)
    No Information Rate : 0.5057         
    P-Value [Acc > NIR] : < 2.2e-16      
                                         
                  Kappa : 0.4503         
 Mcnemar's Test P-Value : < 2.2e-16      
                                         
            Sensitivity : 0.8929         
            Specificity : 0.5558         
         Pos Pred Value : 0.6728         
         Neg Pred Value : 0.8353         
             Prevalence : 0.5057         
         Detection Rate : 0.4515         
   Detection Prevalence : 0.6711         
      Balanced Accuracy : 0.7243         
                                         
       'Positive' Class : no             
                                         

Test Measures given by Logistic Regression Model are as follows:

  1. Accuracy : 72.62%
  2. Sensitivity : 69.82%
  3. Specificity : 75.3%

ROC Curve

par(pty = "s")
info <- roc(ds_train$y, logit_model$fitted.values,plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Logistic Regression",col="#377eb8",lwd=3,print.auc=TRUE)
info

Call:
roc.default(response = ds_train$y, predictor = logit_model$fitted.values,     percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for Logistic Regression",     col = "#377eb8", lwd = 3, print.auc = TRUE)

Data: logit_model$fitted.values in 1828 controls (ds_train$y no) < 1847 cases (ds_train$y yes).
Area under the curve: 87.24%

Time Taken by Logistic Regression Model to Execute

t1 <- Sys.time()
logit_model <- glm(y ~ .,data = ds_train, family = "binomial")
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 0.02214193 secs

Applying KNN Model

knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=1)
accuracy.df <- confusionMatrix(table(knn.pred, valid.df$y))
accuracy.df
Confusion Matrix and Statistics

        
knn.pred  no yes
     no  375 151
     yes 157 369
                                          
               Accuracy : 0.7072          
                 95% CI : (0.6787, 0.7346)
    No Information Rate : 0.5057          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.4144          
 Mcnemar's Test P-Value : 0.7757          
                                          
            Sensitivity : 0.7049          
            Specificity : 0.7096          
         Pos Pred Value : 0.7129          
         Neg Pred Value : 0.7015          
             Prevalence : 0.5057          
         Detection Rate : 0.3565          
   Detection Prevalence : 0.5000          
      Balanced Accuracy : 0.7073          
                                          
       'Positive' Class : no              
                                          

From the above KNN Models, the model with K=11 is the best model as it gives the best accuracy of 78.80%

knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=11)
accuracy.df <- confusionMatrix(table(knn.pred, valid.df$y))
accuracy.df
Confusion Matrix and Statistics

        
knn.pred  no yes
     no  432 123
     yes 100 397
                                          
               Accuracy : 0.788           
                 95% CI : (0.7621, 0.8124)
    No Information Rate : 0.5057          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.5758          
 Mcnemar's Test P-Value : 0.1407          
                                          
            Sensitivity : 0.8120          
            Specificity : 0.7635          
         Pos Pred Value : 0.7784          
         Neg Pred Value : 0.7988          
             Prevalence : 0.5057          
         Detection Rate : 0.4106          
   Detection Prevalence : 0.5276          
      Balanced Accuracy : 0.7877          
                                          
       'Positive' Class : no              
                                          

Time Taken by KNN Model to Execute

t1 <- Sys.time()
knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=11)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 0.1225719 secs

ROC Curve

knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=11,prob = TRUE)
scores.knn <- attr(knn.pred,"prob")
par(pty = "s")
info <- roc(ds_valid$y, scores.knn,plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for KNN Model",col="orange",lwd=3,print.auc=TRUE)
info

Call:
roc.default(response = ds_valid$y, predictor = scores.knn, percent = TRUE,     plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for KNN Model",     col = "orange", lwd = 3, print.auc = TRUE)

Data: scores.knn in 532 controls (ds_valid$y no) < 520 cases (ds_valid$y yes).
Area under the curve: 44.61%

Creating Training and Validation set for other models as these does not take the dummy variables

Applying Classification Tree Model

1. On Whole Dataset

printcp(class.tree)

Classification tree:
rpart(formula = y ~ ., data = t.train.df, method = "class", control = rpart.control(maxdepth = 7), 
    minbucket = 50)

Variables actually used in tree construction:
[1] contact  duration job      month    poutcome

Root node error: 1828/3675 = 0.49741

n= 3675 

        CP nsplit rel error  xerror     xstd
1 0.485777      0   1.00000 1.03118 0.016576
2 0.038840      1   0.51422 0.53939 0.014694
3 0.034737      2   0.47538 0.48195 0.014158
4 0.028446      4   0.40591 0.43545 0.013661
5 0.012582      5   0.37746 0.39880 0.013224
6 0.012035      6   0.36488 0.38348 0.013029
7 0.010000      9   0.32768 0.37309 0.012893
pred.tree <- predict(class.tree, v.valid.df, type = "class")
confusionMatrix(pred.tree,as.factor(v.valid.df$y))
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  425 118
       yes 107 402
                                          
               Accuracy : 0.7861          
                 95% CI : (0.7601, 0.8105)
    No Information Rate : 0.5057          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.5721          
 Mcnemar's Test P-Value : 0.505           
                                          
            Sensitivity : 0.7989          
            Specificity : 0.7731          
         Pos Pred Value : 0.7827          
         Neg Pred Value : 0.7898          
             Prevalence : 0.5057          
         Detection Rate : 0.4040          
   Detection Prevalence : 0.5162          
      Balanced Accuracy : 0.7860          
                                          
       'Positive' Class : no              
                                          

Test Measures given by Classification tree are as follows:

  1. Accuracy : 78.61%
  2. Sensitivity : 79.89%
  3. Specificity : 77.31%

Cross Validation

cv.ct <- rpart(y~ ., data = t.train.df[c(2,9,11,12,16,17)], method = "class", cp = 0.00001, minsplit = 5, xval = 5)
printcp(cv.ct)

Classification tree:
rpart(formula = y ~ ., data = t.train.df[c(2, 9, 11, 12, 16, 
    17)], method = "class", cp = 1e-05, minsplit = 5, xval = 5)

Variables actually used in tree construction:
[1] contact  duration job      month    poutcome

Root node error: 1828/3675 = 0.49741

n= 3675 

           CP nsplit rel error  xerror     xstd
1  4.8578e-01      0   1.00000 1.03173 0.016576
2  3.8840e-02      1   0.51422 0.53392 0.014646
3  3.4737e-02      2   0.47538 0.47046 0.014041
4  2.8446e-02      4   0.40591 0.45624 0.013890
5  1.2582e-02      5   0.37746 0.37199 0.012878
6  1.2035e-02      6   0.36488 0.35613 0.012661
7  5.4705e-03      9   0.32768 0.34081 0.012443
8  3.8293e-03     11   0.31674 0.33753 0.012395
9  3.5558e-03     14   0.30525 0.33425 0.012347
10 2.7352e-03     16   0.29814 0.33425 0.012347
11 2.1882e-03     18   0.29267 0.33042 0.012290
12 1.6411e-03     20   0.28829 0.32877 0.012265
13 1.3676e-03     21   0.28665 0.33589 0.012371
14 1.0941e-03     29   0.27516 0.33753 0.012395
15 9.1174e-04     48   0.25438 0.33643 0.012379
16 8.2057e-04     55   0.24781 0.34245 0.012467
17 7.2939e-04     68   0.23687 0.34300 0.012475
18 6.8381e-04     74   0.23249 0.34300 0.012475
19 5.4705e-04     78   0.22976 0.35886 0.012699
20 4.9234e-04    120   0.20678 0.37035 0.012856
21 4.1028e-04    157   0.18326 0.38950 0.013107
22 3.6470e-04    161   0.18162 0.39059 0.013121
23 3.2823e-04    197   0.16794 0.39059 0.013121
24 3.1260e-04    211   0.16247 0.40263 0.013272
25 2.7352e-04    218   0.16028 0.40536 0.013306
26 2.1882e-04    254   0.15044 0.41028 0.013366
27 2.0514e-04    264   0.14825 0.42013 0.013483
28 1.8235e-04    272   0.14661 0.42013 0.013483
29 1.3676e-04    295   0.14114 0.42779 0.013573
30 1.2157e-04    307   0.13950 0.42888 0.013586
31 1.0941e-04    316   0.13840 0.42888 0.013586
32 7.8149e-05    321   0.13786 0.42888 0.013586
33 1.0000e-05    328   0.13731 0.42888 0.013586

Pruning the Tree

pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
length(pruned.ct$frame$var[pruned.ct$frame$var == "<leaf>"])
[1] 21
prp(pruned.ct, type = 1, extra = 1, split.font = 1, varlen = -10)
Bad 'data' field in model 'call' (expected a data.frame or a matrix).
To silence this warning:
    Call prp with roundint=FALSE,
    or rebuild the rpart model with model=TRUE.

pred.tree <- predict(pruned.ct, v.valid.df, type = "class")
confusionMatrix(pred.tree,as.factor(v.valid.df$y))
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  425 115
       yes 107 405
                                         
               Accuracy : 0.789          
                 95% CI : (0.763, 0.8133)
    No Information Rate : 0.5057         
    P-Value [Acc > NIR] : <2e-16         
                                         
                  Kappa : 0.5778         
 Mcnemar's Test P-Value : 0.6385         
                                         
            Sensitivity : 0.7989         
            Specificity : 0.7788         
         Pos Pred Value : 0.7870         
         Neg Pred Value : 0.7910         
             Prevalence : 0.5057         
         Detection Rate : 0.4040         
   Detection Prevalence : 0.5133         
      Balanced Accuracy : 0.7889         
                                         
       'Positive' Class : no             
                                         

Test Measures after Pruning the tree are as follows:

  1. Accuracy : 78.9%
  2. Sensitivity : 79.89%
  3. Specificity : 77.88%

Time Taken by Classification Tree Model to Execute

t1 <- Sys.time()
cv.ct <- rpart(y~ ., data = t.train.df[-c(1:3,5,6,8,10)], method = "class", cp = 0.00001, minsplit = 5, xval = 5)
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 0.103914 secs

ROC Curve

tree.pred <- predict(pruned.ct, v.valid.df, type = "class")
table_data <- table(prediction = tree.pred,actual=v.valid.df$y)
# Accuracy Metric
sum(diag(table_data))/sum(table_data)
[1] 0.8108365
pred.tree <- predict(pruned.ct, v.valid.df, type = "prob")
par(pty = "s")
info <- roc(v.valid.df$y, pred.tree[,2],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Classification Tree Model",col="purple",lwd=3,print.auc=TRUE)

info

Call:
roc.default(response = v.valid.df$y, predictor = pred.tree[,     2], percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for Classification Tree Model",     col = "purple", lwd = 3, print.auc = TRUE)

Data: pred.tree[, 2] in 532 controls (v.valid.df$y no) < 520 cases (v.valid.df$y yes).
Area under the curve: 83.46%

2. Applying Classification Tree with selected predictors i.e. 1. poutcome

  1. housing
  2. loan
  3. duration

pred.tree <- predict(class.tree, s_validdata, type = "class")
confusionMatrix(pred.tree,as.factor(s_validdata$y))
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  442 152
       yes  90 368
                                          
               Accuracy : 0.77            
                 95% CI : (0.7433, 0.7951)
    No Information Rate : 0.5057          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.5392          
 Mcnemar's Test P-Value : 8.81e-05        
                                          
            Sensitivity : 0.8308          
            Specificity : 0.7077          
         Pos Pred Value : 0.7441          
         Neg Pred Value : 0.8035          
             Prevalence : 0.5057          
         Detection Rate : 0.4202          
   Detection Prevalence : 0.5646          
      Balanced Accuracy : 0.7693          
                                          
       'Positive' Class : no              
                                          

Cross Validation

cv.ct <- rpart(y~ ., data = s_traindata, method = "class", cp = 0.00001, minsplit = 5, xval = 5)
printcp(cv.ct)

Classification tree:
rpart(formula = y ~ ., data = s_traindata, method = "class", 
    cp = 1e-05, minsplit = 5, xval = 5)

Variables actually used in tree construction:
[1] duration housing  loan     poutcome

Root node error: 1828/3675 = 0.49741

n= 3675 

           CP nsplit rel error  xerror     xstd
1  0.48577681      0   1.00000 1.00000 0.016581
2  0.03719912      1   0.51422 0.53446 0.014651
3  0.03227571      3   0.43982 0.45624 0.013890
4  0.00984683      4   0.40755 0.42505 0.013541
5  0.00382932      5   0.39770 0.40372 0.013285
6  0.00200584      9   0.38239 0.40263 0.013272
7  0.00164114     14   0.37090 0.40317 0.013279
8  0.00127644     21   0.35886 0.40481 0.013299
9  0.00123085     24   0.35503 0.40810 0.013339
10 0.00109409     28   0.35011 0.40810 0.013339
11 0.00082057     34   0.34354 0.41028 0.013366
12 0.00072939     44   0.33534 0.42177 0.013503
13 0.00063822     49   0.33151 0.43217 0.013623
14 0.00054705     55   0.32768 0.43217 0.013623
15 0.00036470     89   0.30908 0.44201 0.013735
16 0.00031260    138   0.28720 0.46444 0.013978
17 0.00027352    147   0.28392 0.46608 0.013995
18 0.00023445    213   0.26477 0.47867 0.014125
19 0.00021882    220   0.26313 0.48687 0.014207
20 0.00019893    225   0.26204 0.48687 0.014207
21 0.00018235    242   0.25821 0.48796 0.014218
22 0.00013676    257   0.25547 0.49234 0.014261
23 0.00001000    275   0.25274 0.49781 0.014314

Pruning the Tree

pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
length(pruned.ct$frame$var[pruned.ct$frame$var == "<leaf>"])
[1] 10
prp(pruned.ct, type = 1, extra = 1, split.font = 1, varlen = -10)

pred.tree <- predict(pruned.ct, s_validdata, type = "class")
confusionMatrix(pred.tree,as.factor(s_validdata$y))
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  442 146
       yes  90 374
                                          
               Accuracy : 0.7757          
                 95% CI : (0.7492, 0.8005)
    No Information Rate : 0.5057          
    P-Value [Acc > NIR] : < 2.2e-16       
                                          
                  Kappa : 0.5507          
 Mcnemar's Test P-Value : 0.0003433       
                                          
            Sensitivity : 0.8308          
            Specificity : 0.7192          
         Pos Pred Value : 0.7517          
         Neg Pred Value : 0.8060          
             Prevalence : 0.5057          
         Detection Rate : 0.4202          
   Detection Prevalence : 0.5589          
      Balanced Accuracy : 0.7750          
                                          
       'Positive' Class : no              
                                          

Time Taken by Classification Tree Model to Execute

t1 <- Sys.time()
cv.ct <- rpart(y~ ., data = s_traindata, method = "class", cp = 0.00001, minsplit = 5, xval = 5)
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 0.1059 secs

ROC Curve

pred.tree <- predict(pruned.ct, s_validdata, type = "prob")
par(pty = "s")
info <- roc(s_validdata$y, pred.tree[,2],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Classification Tree Model",col="purple",lwd=3,print.auc=TRUE)
info

Call:
roc.default(response = s_validdata$y, predictor = pred.tree[,     2], percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for Classification Tree Model",     col = "purple", lwd = 3, print.auc = TRUE)

Data: pred.tree[, 2] in 532 controls (s_validdata$y no) < 520 cases (s_validdata$y yes).
Area under the curve: 84.79%

Applying Random Forest Model

rf <- randomForest(as.factor(y) ~ ., data = s_traindata, ntree = 100,
mtry = 4, nodesize = 5, importance = TRUE)
rf

Call:
 randomForest(formula = as.factor(y) ~ ., data = s_traindata,      ntree = 100, mtry = 4, nodesize = 5, importance = TRUE) 
               Type of random forest: classification
                     Number of trees: 100
No. of variables tried at each split: 4

        OOB estimate of  error rate: 24.03%
Confusion matrix:
      no  yes class.error
no  1385  443   0.2423414
yes  440 1407   0.2382241

rf.pred <- predict(rf, s_validdata)
confusionMatrix(rf.pred, s_validdata$y)
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  421 141
       yes 111 379
                                         
               Accuracy : 0.7605         
                 95% CI : (0.7335, 0.786)
    No Information Rate : 0.5057         
    P-Value [Acc > NIR] : < 2e-16        
                                         
                  Kappa : 0.5205         
 Mcnemar's Test P-Value : 0.06773        
                                         
            Sensitivity : 0.7914         
            Specificity : 0.7288         
         Pos Pred Value : 0.7491         
         Neg Pred Value : 0.7735         
             Prevalence : 0.5057         
         Detection Rate : 0.4002         
   Detection Prevalence : 0.5342         
      Balanced Accuracy : 0.7601         
                                         
       'Positive' Class : no             
                                         

Test Measures given by Random Forest Model are as follows:

  1. Accuracy : 83.56%
  2. Sensitivity : 82.33%
  3. Specificity : 84.81%

Time Taken by Random Forest Model to Execute

t1 <- Sys.time()
rf <- randomForest(as.factor(y) ~ ., data = s_traindata, ntree = 100,
mtry = 4, nodesize = 5, importance = TRUE)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 0.445951 secs

ROC Curve

par(pty = "s")
info <- roc(s_traindata$y, rf$votes[,1],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Random Forest Model",col="#4daf4a",lwd=3,print.auc=TRUE)
info

Call:
roc.default(response = s_traindata$y, predictor = rf$votes[,     1], percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for Random Forest Model",     col = "#4daf4a", lwd = 3, print.auc = TRUE)

Data: rf$votes[, 1] in 1828 controls (s_traindata$y no) > 1847 cases (s_traindata$y yes).
Area under the curve: 83.45%

Boosting the Tree

s_traindata$y <- as.factor(s_traindata$y)
set.seed(1)
boost <- boosting(y ~ ., data = s_traindata)
pred <- predict(boost, s_validdata)
confusionMatrix(as.factor(pred$class), as.factor(s_validdata$y))
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  430  98
       yes 102 422
                                          
               Accuracy : 0.8099          
                 95% CI : (0.7848, 0.8332)
    No Information Rate : 0.5057          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.6198          
 Mcnemar's Test P-Value : 0.832           
                                          
            Sensitivity : 0.8083          
            Specificity : 0.8115          
         Pos Pred Value : 0.8144          
         Neg Pred Value : 0.8053          
             Prevalence : 0.5057          
         Detection Rate : 0.4087          
   Detection Prevalence : 0.5019          
      Balanced Accuracy : 0.8099          
                                          
       'Positive' Class : no              
                                          

Test Measures given after Boosting the tree are as follows:

  1. Accuracy : 80.99%
  2. Sensitivity : 80.83%
  3. Specificity : 81.15%

Time Taken by Random Forest Model to Execute After Boosting the Tree

t1 <- Sys.time()
rf <- boost <- boosting(y ~ ., data = s_traindata)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 29.59273 secs

ROC Curve

par(pty = "s")
info <- roc(s_traindata$y, boost$votes[,1],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Boosted Tree",col="#4daf4a",lwd=3,print.auc=TRUE)
info

Call:
roc.default(response = s_traindata$y, predictor = boost$votes[,     1], percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for Boosted Tree",     col = "#4daf4a", lwd = 3, print.auc = TRUE)

Data: boost$votes[, 1] in 1828 controls (s_traindata$y no) > 1847 cases (s_traindata$y yes).
Area under the curve: 89.64%

Interpretation:

Based on the Accuracy and Confidence Interval, the following models seems convenient and will be used further. 1. KNN (with K=11) 2. Classification Tree(Pruned) 3. Random Forest Classification Model(Boosted)

Now we will implement these models on the test data with the relevant predictors

1. KNN Model

knn.pred <- knn(train=ds_train[,-5],test = ds_test[,-5], cl =
                  ds_train$y, k=11)
accuracy.df <- confusionMatrix(table(knn.pred, ds_test$y))
accuracy.df
Confusion Matrix and Statistics

        
knn.pred  no yes
     no  224  71
     yes  60 206
                                          
               Accuracy : 0.7665          
                 95% CI : (0.7292, 0.8009)
    No Information Rate : 0.5062          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.5327          
 Mcnemar's Test P-Value : 0.3823          
                                          
            Sensitivity : 0.7887          
            Specificity : 0.7437          
         Pos Pred Value : 0.7593          
         Neg Pred Value : 0.7744          
             Prevalence : 0.5062          
         Detection Rate : 0.3993          
   Detection Prevalence : 0.5258          
      Balanced Accuracy : 0.7662          
                                          
       'Positive' Class : no              
                                          

ROC Curve

par(pty = "s")
info <- roc(ds_test$y, scores.knn,plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for KNN Model",col="orange",lwd=3,print.auc=TRUE)
info

Call:
roc.default(response = ds_test$y, predictor = scores.knn, percent = TRUE,     plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for KNN Model",     col = "orange", lwd = 3, print.auc = TRUE)

Data: scores.knn in 284 controls (ds_test$y no) < 277 cases (ds_test$y yes).
Area under the curve: 41.84%

Time Taken by KNN Model to Execute

t1 <- Sys.time()
knn.pred <- knn(train=ds_train[,-5],test = ds_test[,-5], cl =
                  ds_train$y, k=11)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 0.08148694 secs

2. Classification Tree(Pruned) Model

Cross Validation

cv.ct <- rpart(y~ ., data = s_traindata, method = "class", cp = 0.00001, minsplit = 5, xval = 5)
printcp(cv.ct)

Classification tree:
rpart(formula = y ~ ., data = s_traindata, method = "class", 
    cp = 1e-05, minsplit = 5, xval = 5)

Variables actually used in tree construction:
[1] duration housing  loan     poutcome

Root node error: 1828/3675 = 0.49741

n= 3675 

           CP nsplit rel error  xerror     xstd
1  0.48577681      0   1.00000 1.03611 0.016574
2  0.03719912      1   0.51422 0.51860 0.014509
3  0.03227571      3   0.43982 0.45952 0.013926
4  0.00984683      4   0.40755 0.41028 0.013366
5  0.00382932      5   0.39770 0.40427 0.013292
6  0.00200584      9   0.38239 0.39934 0.013231
7  0.00164114     14   0.37090 0.40317 0.013279
8  0.00127644     21   0.35886 0.39989 0.013238
9  0.00123085     24   0.35503 0.40098 0.013252
10 0.00109409     28   0.35011 0.40098 0.013252
11 0.00082057     34   0.34354 0.40153 0.013258
12 0.00072939     44   0.33534 0.40536 0.013306
13 0.00063822     49   0.33151 0.40919 0.013352
14 0.00054705     55   0.32768 0.40919 0.013352
15 0.00036470     89   0.30908 0.42560 0.013548
16 0.00031260    138   0.28720 0.45405 0.013867
17 0.00027352    147   0.28392 0.45569 0.013884
18 0.00023445    213   0.26477 0.45842 0.013914
19 0.00021882    220   0.26313 0.46718 0.014006
20 0.00019893    225   0.26204 0.46718 0.014006
21 0.00018235    242   0.25821 0.46772 0.014012
22 0.00013676    257   0.25547 0.47921 0.014130
23 0.00001000    275   0.25274 0.48414 0.014180

Pruning the Tree

pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
length(pruned.ct$frame$var[pruned.ct$frame$var == "<leaf>"])
[1] 10
prp(pruned.ct, type = 1, extra = 1, split.font = 1, varlen = -10)

pred.tree <- predict(pruned.ct, s_testdata, type = "class")
confusionMatrix(pred.tree,as.factor(s_testdata$y))
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  228  83
       yes  56 194
                                          
               Accuracy : 0.7522          
                 95% CI : (0.7143, 0.7874)
    No Information Rate : 0.5062          
    P-Value [Acc > NIR] : < 2e-16         
                                          
                  Kappa : 0.5038          
 Mcnemar's Test P-Value : 0.02743         
                                          
            Sensitivity : 0.8028          
            Specificity : 0.7004          
         Pos Pred Value : 0.7331          
         Neg Pred Value : 0.7760          
             Prevalence : 0.5062          
         Detection Rate : 0.4064          
   Detection Prevalence : 0.5544          
      Balanced Accuracy : 0.7516          
                                          
       'Positive' Class : no              
                                          

Time Taken by Classification Tree Model to Execute

t1 <- Sys.time()
cv.ct <- rpart(y~ ., data = s_traindata, method = "class", cp = 0.00001, minsplit = 5, xval = 5)
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 0.1881721 secs

ROC Curve

pred.tree <- predict(pruned.ct, s_testdata, type = "prob")
par(pty = "s")
info <- roc(s_testdata$y, pred.tree[,2],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Pruned Tree Model",col="purple",lwd=3,print.auc=TRUE)
info

Call:
roc.default(response = s_testdata$y, predictor = pred.tree[,     2], percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for Pruned Tree Model",     col = "purple", lwd = 3, print.auc = TRUE)

Data: pred.tree[, 2] in 284 controls (s_testdata$y no) < 277 cases (s_testdata$y yes).
Area under the curve: 84.05%

3. Random Forest Classification Model(Boosted)

rf <- randomForest(as.factor(y) ~ ., data = s_traindata, ntree = 100,
mtry = 4, nodesize = 5, importance = TRUE)
rf.pred <- predict(rf, s_testdata)
confusionMatrix(rf.pred, s_testdata$y)
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  220  85
       yes  64 192
                                          
               Accuracy : 0.7344          
                 95% CI : (0.6958, 0.7705)
    No Information Rate : 0.5062          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.4682          
 Mcnemar's Test P-Value : 0.1013          
                                          
            Sensitivity : 0.7746          
            Specificity : 0.6931          
         Pos Pred Value : 0.7213          
         Neg Pred Value : 0.7500          
             Prevalence : 0.5062          
         Detection Rate : 0.3922          
   Detection Prevalence : 0.5437          
      Balanced Accuracy : 0.7339          
                                          
       'Positive' Class : no              
                                          

Boosting the Tree

pred <- predict(boost, s_testdata)
confusionMatrix(as.factor(pred$class), as.factor(s_testdata$y))
Confusion Matrix and Statistics

          Reference
Prediction  no yes
       no  224  54
       yes  60 223
                                          
               Accuracy : 0.7968          
                 95% CI : (0.7611, 0.8293)
    No Information Rate : 0.5062          
    P-Value [Acc > NIR] : <2e-16          
                                          
                  Kappa : 0.5936          
 Mcnemar's Test P-Value : 0.6396          
                                          
            Sensitivity : 0.7887          
            Specificity : 0.8051          
         Pos Pred Value : 0.8058          
         Neg Pred Value : 0.7880          
             Prevalence : 0.5062          
         Detection Rate : 0.3993          
   Detection Prevalence : 0.4955          
      Balanced Accuracy : 0.7969          
                                          
       'Positive' Class : no              
                                          

ROC Curve

par(pty = "s")
info <- roc(s_traindata$y, boost$votes[,1],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Boosted Tree",col="#4daf4a",lwd=3,print.auc=TRUE)
info

Call:
roc.default(response = s_traindata$y, predictor = boost$votes[,     1], percent = TRUE, plot = TRUE, legacy.axes = TRUE, xlab = "False Positive Percentage",     ylab = "True Positive Percentage", main = "ROC Curve for Boosted Tree",     col = "#4daf4a", lwd = 3, print.auc = TRUE)

Data: boost$votes[, 1] in 1828 controls (s_traindata$y no) > 1847 cases (s_traindata$y yes).
Area under the curve: 89.64%

Time Taken by Random Forest Model to Execute After Boosting the Tree

t1 <- Sys.time()
rf <- boost <- boosting(y ~ ., data = s_traindata)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
Time difference of 29.77382 secs

Interpretations:

  1. Boosted tree gave the maximum Area Under Curve and best accuracy but also took maximum time to execute.
  2. Pruned Tree also gave accuracy close to Boosted tree and 84.2% area under the curve. It also took very less time to execute as compared to the Boosted tree.
  3. The KNN model took the least time to execute and a good accuracy but it gave worst Area Under Curve i.e 41.8%.

Overall Boosted Tree seems to be the most convenient model to be used for this application.

---
title: "Project"
output: html_notebook
---

```{r}
library(dummies)
library(ltm)
library(dplyr)
library(readxl)
library(psych)
require(MASS)
library(FNN)
library(adabag)
library(rpart) 
library(caret)
library(randomForest)
library(party)
library(ROCR)
library(ggplot2)
library(rpart.plot)
library(pROC)
```

```{r}
bank <- bank.full
View(bank)
```
# checking the missing values in the dataset
```{r}
sum(is.na(bank))
```

# Performing OverSampling to Data as the number of success cases are very less as compared to the failure cases
```{r}
success <- bank[bank$y == "yes",]
success
nrow(success)
```
```{r}
failure <- bank[bank$y == "no",]
failure
nrow(failure)
```
```{r}
set.seed(1)
bank_success <- success[sample(nrow(success),nrow(success)/2),]
bank_success
```
```{r}
set.seed(1)
bank_failure <- failure[sample(nrow(failure),nrow(success)/2),]
bank_failure
```
```{r}
bank_data <- rbind(bank_success,bank_failure)
t_bank_data <- rbind(bank_success,bank_failure)
bank_data
```
```{r}
summary(bank_data)
```

# Converting Job variable into dummy variable
```{r}
#install.packages("dummies")
cbind(bank_data,dummy(bank_data$job, sep = "_"))
job_dummy <- dummy(bank_data$job, sep = "_")
job_dummy

bank_data$job <- NULL
bank_data
```
# Converting Marital variable into dummy variable
```{r}
cbind(bank_data,dummy(bank_data$marital, sep = "_"))
marital_dummy <- dummy(bank_data$marital, sep = "_")
marital_dummy

bank_data$marital <- NULL
bank_data
```
# Converting Education variable into dummy variable
```{r}
cbind(bank_data,dummy(bank_data$education, sep = "_"))
education_dummy <- dummy(bank_data$education, sep = "_")
education_dummy

bank_data$education <- NULL
bank_data
```
# Converting Contact variable into dummy variable
```{r}
cbind(bank_data,dummy(bank_data$contact, sep = "_"))
education_dummy <- dummy(bank_data$contact, sep = "_")
education_dummy

bank_data$contact <- NULL
bank_data
```
# Converting Month variable into dummy variable
```{r}
cbind(bank_data,dummy(bank_data$month, sep = "_"))
month_dummy <- dummy(bank_data$month, sep = "_")
month_dummy

bank_data$month <- NULL
bank_data
```
# Converting poutcome variable into dummy variable
```{r}
bank_data$poutcome <- as.numeric(as.character(factor(bank_data$poutcome,levels=c('failure','success','other','unknown'),labels =c(0,1,2,4) )))
                    
bank_data$poutcome

#cbind(bank_data,dummy(bank_data$poutcome, sep = "_"))
#pouctome_dummy <- dummy(bank_data$poutcome, sep = "_")
#pouctome_dummy

#bank_data$poutcome <- NULL
bank_data
```
# Converting default categorical variable into numerical
```{r}
bank_data$default <- as.numeric(as.character(factor(bank_data$default,levels=c('yes','no'),
                    labels =c(1,0) )))
bank_data$default
```
# Converting housing categorical variable into numerical
```{r}
bank_data$housing <- as.numeric(as.character(factor(bank_data$housing,levels=c('yes','no'),
                    labels =c(1,0) )))
bank_data$housing
```
# Converting loan categorical variable into numerical
```{r}
bank_data$loan <- as.numeric(as.character(factor(bank_data$loan,levels = c('yes','no'),
                    labels =c(1,0) )))
bank_data$loan
```
# Converting y categorical variable into numerical
```{r}
#bank_data$y <- as.numeric(as.character(factor(bank_data$y,levels = c('yes','no'),
#                    labels =c(1,0))))
#bank_data$y
```
```{r}
View(bank_data)
```
# Creating New Dataset with the required Columns
```{r}
bank_dataset <- cbind(job_dummy,marital_dummy,education_dummy,month_dummy,(bank_data))
View(bank_dataset)
```
# Normalising the data
```{r}
normalize_data <- bank_dataset
norm.values <- preProcess(normalize_data[,1:41], method = c("center", "scale"))
normalize_data[,1:41] <- predict(norm.values, normalize_data[,1:41])
View(normalize_data)
```

# Dividing Data into Training Set, Validation Set and Test Set
```{r}
set.seed(1)
idx <- sample(seq(1, 3), size = nrow(normalize_data), replace = TRUE, prob = c(.7, .2, .1))
train.df <- normalize_data[idx == 1,]
valid.df <- normalize_data[idx == 2,]
test.df <- normalize_data[idx == 3,]

nrow(train.df)
nrow(valid.df)
nrow(test.df)
#train.index <- sample(row.names(normalize_data),0.8*dim(normalize_data)[1])
#valid.index <- setdiff(row.names(normalize_data),train.index)
#train.df <- normalize_data[train.index,]
#valid.df <- normalize_data[valid.index,]
```
# Point Biserial Correlation to find relation between continuous and categorical data
install.packages("ltm")
```{r}
cont_data <- cbind(bank.full[,c(1,6,10,12:15)])
cat_data <- cbind(bank.full[,c(2:5,7:9,11,16)])
View(cat_data)
View(cont_data)
biserial.cor(bank.full$age, bank.full$y, use = c("all.obs", "complete.obs"), level = 1)
``` 

# Finding Correlations Among Continuous Variable
```{r}
cor_data <- train.df[c('age','balance','day','duration','campaign','pdays','previous')]
cor(cor_data)
```
# Boxplots (Comparing the Predictors with the Output Variable)
```{r}
par(mfrow=c(2,2))
boxplot(train.df$age ~ train.df$y,main="Age", col=c('powderblue', 'mistyrose'))
boxplot(train.df$balance ~ train.df$y,main="Balance", col=c('powderblue', 'mistyrose'))
boxplot(train.df$day ~ train.df$y,main="day", col=c('powderblue', 'mistyrose'))
boxplot(train.df$duration ~ train.df$y,main="duration", col=c('powderblue', 'mistyrose'))
boxplot(train.df$campaign ~ train.df$y,main="campaign", col=c('powderblue', 'mistyrose'))
boxplot(train.df$pdays ~ train.df$y,main="pdays", col=c('powderblue', 'mistyrose'))
boxplot(train.df$previous ~ train.df$y,main="previous", col=c('powderblue', 'mistyrose'))
```
# Interpretation for Box Plots
1. Longer Duration of calls results in that person will deposit in the bank
2. Pdays i.e. person contacted in the past also contibutes to outcome variable
3. People contacted more leads to person deposition in the bank
4. Other predictors including Age and Balance very much impacts the outcome variable in both      i.e. yes and no

# Function for Multiplot to plot multiple graphs together
```{r}
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
  library(grid)

  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)

  numPlots = length(plots)

  # If layout is NULL, then use 'cols' to determine layout
  if (is.null(layout)) {
    # Make the panel
    # ncol: Number of columns of plots
    # nrow: Number of rows needed, calculated from # of cols
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                    ncol = cols, nrow = ceiling(numPlots/cols))
  }

 if (numPlots==1) {
    print(plots[[1]])

  } else {
    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))

    # Make each plot, in the correct location
    for (i in 1:numPlots) {
      # Get the i,j matrix positions of the regions that contain this subplot
      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))

      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                      layout.pos.col = matchidx$col))
    }
  }
}
```

# GGPLOT for Marital with the other predictors    
```{r}
marital_duration<-summarise(group_by(bank.full,marital),duration=mean(duration))
marital_duration
p1<-ggplot(marital_duration,aes(x=marital,y=duration,fill=marital))+
geom_bar(stat='identity')

marital_balance<-summarise(group_by(bank.full,marital),balance=mean(balance))
marital_balance
p2<-ggplot(marital_balance,aes(x=marital,y=balance,fill=marital))+ geom_bar(stat='identity')

marital_age<-summarise(group_by(bank.full,marital),age=mean(age))
marital_age
p3<-ggplot(marital_age,aes(x=marital,y=age,fill=marital))+ geom_bar(stat='identity')

marital_pdays<-summarise(group_by(bank.full,marital),pdays=mean(pdays))
marital_pdays
p4<-ggplot(marital_pdays,aes(x=marital,y=pdays,fill=marital))+ geom_bar(stat='identity')

multiplot(p1, p2, p3, p4, cols=2)
```

# GGPLOT for Job Variable with the other predictors  
```{r}
job_duration<-summarise(group_by(bank.full,job),duration=mean(duration))
job_duration
p1<-ggplot(job_duration,aes(x=job,y=duration,fill=job))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 0.5))

job_balance<-summarise(group_by(bank.full,job),balance=mean(balance))
job_balance
p2<-ggplot(job_balance,aes(x=job,y=balance,fill=job))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 0.5))

job_age<-summarise(group_by(bank.full,job),age=mean(age))
job_age
p3<-ggplot(job_age,aes(x=job,y=age,fill=job))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 0.5))

job_pdays<-summarise(group_by(bank.full,job),pdays=mean(pdays))
job_pdays
p4<-ggplot(job_pdays,aes(x=job,y=pdays,fill=job))+
geom_bar(stat='identity')+theme(axis.text.x = element_text(angle = 45,
hjust = 1, vjust = 0.5))

multiplot(p1, p2, p3, p4, cols=2)
```

# GGPLOT for Education with the other predictors  
```{r}
education_duration<-summarise(group_by(bank.full,education),duration=mean(duration))
education_duration
p1<-ggplot(education_duration,aes(x=education,y=duration,fill=education))+
geom_bar(stat='identity')

education_balance<-summarise(group_by(bank.full,education),balance=mean(balance))
education_balance
p2<-ggplot(education_balance,aes(x=education,y=balance,fill=education))+
geom_bar(stat='identity')

education_age<-summarise(group_by(bank.full,education),age=mean(age))
education_age
p3<-ggplot(education_age,aes(x=education,y=age,fill=education))+ geom_bar(stat='identity')

education_pdays<-summarise(group_by(bank.full,education),age=mean(pdays))
education_pdays
p4<-ggplot(education_pdays,aes(x=education,y=age,fill=education))+ geom_bar(stat='identity')

multiplot(p1, p2, p3, p4, cols=2)
```

# GGPLOT for outcome Y with the other predictors  
```{r}
ggplot(bank.full,aes(x=education,fill=education))+ geom_bar(stat='count',aes(fill =
factor(y)),position = position_dodge(width = 0.9))

ggplot(bank.full,aes(x=marital,fill=marital))+ geom_bar(stat='count',aes(fill =
factor(y)),position = position_dodge(width = 0.9))

ggplot(bank.full,aes(x=job,fill=job))+ geom_bar(stat='count',aes(fill =
factor(y)),position = position_dodge(width = 0.9))+theme(axis.text.x =
element_text(angle = 45, hjust = 1, vjust = 0.5))

ggplot(bank.full,aes(x=contact,fill=contact))+ geom_bar(stat='count',aes(fill =
factor(y)),position = position_dodge(width = 0.9))
```

# Contact vs Age Predictor
```{r}
ggplot(bank.full,aes(x=contact,fill=contact))+ geom_bar(stat='count',aes(fill =
factor(age)),position = position_dodge(width = 0.9))
```

# Scatter Plot Between Age and other Continuous Variables
```{r}
par(mfrow=c(2,2))
plot(log(train.df$age), log(train.df$balance), main = "Age Vs Balance", xlab = "Age", ylab = "Balance", col = 2)
abline(lm(log(train.df$balance) ~ log(train.df$age)))

plot(log(train.df$age), log(train.df$duration), main = "Age Vs Duration", xlab = "Age", ylab = "Duration", col = 2)
abline(lm(log(train.df$duration) ~ log(train.df$age)))

plot(log(train.df$age), log(train.df$pdays), main = "Age Vs Days Past", xlab = "Age", ylab = "Days Past", col = 2)
abline(lm(log(train.df$pdays) ~ log(train.df$age)))

plot(log(train.df$age), log(train.df$previous), main = "Age Vs Previously Contacted", xlab = "Age", ylab = "Previously Contacted", col = 2)
abline(lm(log(train.df$previous) ~ log(train.df$age)))

plot(log(train.df$age), log(train.df$day), main = "Age Vs Day", xlab = "Age", ylab = "Day", col = 2)
abline(lm(log(train.df$day) ~ log(train.df$age)))

plot(log(train.df$age), log(train.df$campaign), main = "Age Vs Campaign", xlab = "Age", ylab = "Campaign", col = 2)
abline(lm(log(train.df$campaign) ~ log(train.df$age)))
```
# Scatter Plot Between Balance and other Continuous Variables
```{r}
par(mfrow=c(2,2))
plot(log(train.df$balance), log(train.df$duration), main = "Duration Vs Balance", xlab = "Balance", ylab = "Duration", col = 2)
abline(lm(log(train.df$duration) ~ log(train.df$balance)))

plot(log(train.df$duration), log(train.df$pdays), main = "Duration Vs Days Past", xlab = "Duration", ylab = "Days Past", col = 2)
abline(lm(log(train.df$pdays) ~ log(train.df$duration)))

plot(log(train.df$duration), log(train.df$previous), main = "Duration Vs Previously Contacted", xlab = "Duration", ylab = "Previously Contacted", col = 2)
abline(lm(log(train.df$previous) ~ log(train.df$duration)))

plot(log(train.df$duration), log(train.df$day), main = "Duration Vs Day", xlab = "Duration", ylab = "Day", col = 2)
abline(lm(log(train.df$day) ~ log(train.df$duration)))

plot(log(train.df$duration), log(train.df$campaign), main = "Duration Vs Campaign", xlab = "Duration", ylab = "Campaign", col = 2)
abline(lm(log(train.df$campaign) ~ log(train.df$duration)))
```

# Interpretation for Scatter Plots
1. Balance have a positive relation with the Age
2. Days, Duration, Pdays, Previous, Day and Campaign have a negative or no relation with the      Age predictor
3. Campagin has positive relation with the Duration
4. Balance have negative relation with the Duration
5. Pdays, Previous and Day have no relation with the Duration predictor

# Principal Component Analysis on Continuous Predictors
install.packages("readxl")
install.packages('psych')
```{r}
#install.packages("readxl")
#install.packages('psych')
normal_data <- train.df[,c(31,33,36:40)]
fa.parallel(normal_data, fm="pa", main = "Scree Plot With Parallel Analysis")
```
```{r}
pc <- principal(r = normal_data, nfactor = 3, rotate = "none")
pc
```
# Performing Rotation
```{r}
pc_rotate <- principal(cont_data, nfactor = 3, rotate = "varimax")
pc_rotate
```
```{r}
pc_score <- principal(normal_data, nfactor = 3, scores = TRUE)
head(pc_score$scores)
```
```{r}
factor.plot(pc_rotate, labels = rownames(pc_rotate$loadings))
```

# Interpretation
1. Parallel Analysis suggests that Number of components to extract should be equal to 3 since 3 variables have eigenvalue greater than 1.
2. Together all the components accounts to about 40 percent of the cumulative Variance both before and after rotating the components.
3. There is no such major changes in the Proportion and Cumulative variance after rotating the components.
4. The loading in RC1 indicates that first component is primarily defined by pdays and            previous variables.
5. While loading in RC2 indicates that second component is primarily defined by day and campaign

# Applying Models

# Applying Logistic Regression Model to select the relevant predictors (Dimension Reduction)
```{r}
logit_model <- glm(y ~ .,data = train.df, family = binomial(link = "logit"))
summary(logit_model)
```

```{r}
# Calculating Odds Value
#data.frame(summary(logit_model)$coefficients, odds = exp(coef(logit_model)))
#round(data.frame(summary(logit_model)$coefficients, odds = exp(coef(logit_model))), 5)
data.frame(exp(coef(logit_model))) 
```

# From the Logistic Regression Model, the following predictors have significant impact on the outcome i.e. either positive or negative:
1. contact_cellular
2. contact_telephone
3. month_aug
4. month_jan
5. month_jul
6. month_may
7. month_nov
8. poutcome
9. housing
10. loan
11. duration

```{r}
predicted <- predict(logit_model, valid.df, type = "link")
predicted
result <- ifelse(predicted > 0.5,"yes","no")

confusionMatrix(as.factor(result), valid.df$y)
```
# Based on Domain Knowldege the following selected predictors does not seem relevant predictors to train or make the model :
1. contact_cellular
2. contact_telephone
3. month_aug
4. month_jan
5. month_jul
6. month_may
7. month_nov

# There, we are left with the following predictors
1. poutcome
2. housing
3. loan
4. duration

# Dataset with the required predictors
```{r}
testing_data <- data.frame(train.df[,c(16,17,20,23,24,27,28,34,35,37,41,42)])
testing_data
testing_valid <- data.frame(valid.df[,c(16,17,20,23,24,27,28,34,35,37,41,42)])
testing_valid

ds_train <- data.frame(train.df[,c(34,35,37,41,42)])
ds_train

ds_valid <- data.frame(valid.df[,c(34,35,37,41,42)])
ds_valid

ds_test <- data.frame(test.df[,c(34,35,37,41,42)])
ds_test
```

# Logistic Regression Model

# With all the predictors given by Logistic Regression Model
```{r}
logit_model <- glm(y ~ .,data = testing_data, family = "binomial")
summary(logit_model)
predicted <- predict(logit_model, testing_valid, type = "link")
predicted
result <- ifelse(predicted > 0.5,"yes","no")

confusionMatrix(as.factor(result), valid.df$y)
```

# Based on the predictors given by Logistic Regression Model and Domain Knowldege
```{r}
logit_model <- glm(y ~ .,data = ds_train, family = "binomial")
summary(logit_model)
predicted <- predict(logit_model, ds_valid, type = "link")
predicted
result <- ifelse(predicted > 0.5,"yes","no")

confusionMatrix(as.factor(result), valid.df$y)
```
# Test Measures given by Logistic Regression Model are as follows:
1. Accuracy : 72.62%
2. Sensitivity : 69.82% 
3. Specificity : 75.3% 

# ROC Curve
```{r}
#install.packages('pROC')
par(pty = "s")
info <- roc(ds_train$y, logit_model$fitted.values,plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Logistic Regression",col="#377eb8",lwd=3,print.auc=TRUE)
info
```
```{r}
roc.df <- data.frame(tpp=info$sensitivities*100,
                     fpp=(1-info$specificities)*100,
                     thresholds=info$thresholds)
head(roc.df)
```
```{r}
tail(roc.df)
```

# Time Taken by Logistic Regression Model to Execute
```{r}
t1 <- Sys.time()
logit_model <- glm(y ~ .,data = ds_train, family = "binomial")
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```

# Applying KNN Model
```{r}
#install.packages('FNN')
knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=1)
accuracy.df <- confusionMatrix(table(knn.pred, valid.df$y))
accuracy.df
```
```{r}
#install.packages('FNN')

accuracy.df <- data.frame(k=seq(1,14,1), accuracy = rep(0,14))

for(i in 1:14){
  
  knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=i)
  
 accuracy.df[i,2]<- confusionMatrix(table(knn.pred,valid.df$y))$overall[1]
}
accuracy.df
```
# From the above KNN Models, the model with K=11 is the best model as it gives the best accuracy of 78.80%
```{r}
#install.packages('FNN')
knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=11)
accuracy.df <- confusionMatrix(table(knn.pred, valid.df$y))
accuracy.df
```

# Time Taken by KNN Model to Execute
```{r}
t1 <- Sys.time()
knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=11)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```

# ROC Curve
```{r}
knn.pred <- knn(train=ds_train[,-5],test = ds_valid[,-5], cl =
                  ds_train$y, k=11,prob = TRUE)
scores.knn <- attr(knn.pred,"prob")

par(pty = "s")
info <- roc(ds_valid$y, scores.knn,plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for KNN Model",col="orange",lwd=3,print.auc=TRUE)
info
```

# Creating Training and Validation set for other models as these does not take the dummy variables
```{r}
f_data <- t_bank_data

set.seed(1)
idx <- sample(seq(1, 3), size = nrow(f_data), replace = TRUE, prob = c(.7, .2, .1))
t.train.df <- f_data[idx == 1,]
v.valid.df <- f_data[idx == 2,]
t.test.df <- f_data[idx == 3,]
#t.train.index <- sample(row.names(f_data),0.8*dim(f_data)[1])
#v.valid.index <- setdiff(row.names(f_data),t.train.index)
#t.train.df <- f_data[t.train.index,]
#v.valid.df <- f_data[v.valid.index,]
```

# Applying Classification Tree Model
#1. On Whole Dataset
```{r}
class.tree <- rpart(y ~ .,data = t.train.df, control = rpart.control(maxdepth = 7), method = "class", minbucket = 50)
prp(class.tree, type = 1, extra = 1, under = TRUE, split.font = 1, varlen = -10)
```
```{r}
printcp(class.tree)
```

```{r}
pred.tree <- predict(class.tree, v.valid.df, type = "class")
confusionMatrix(pred.tree,as.factor(v.valid.df$y))
```
# Test Measures given by Classification tree are as follows:
1. Accuracy : 78.61%
2. Sensitivity : 79.89% 
3. Specificity : 77.31% 

# Cross Validation
```{r}
cv.ct <- rpart(y~ ., data = t.train.df[c(2,9,11,12,16,17)], method = "class", cp = 0.00001, minsplit = 5, xval = 5)
printcp(cv.ct)
```

# Pruning the Tree
```{r}
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
length(pruned.ct$frame$var[pruned.ct$frame$var == "<leaf>"])
prp(pruned.ct, type = 1, extra = 1, split.font = 1, varlen = -10)
```
```{r}
pred.tree <- predict(pruned.ct, v.valid.df, type = "class")
confusionMatrix(pred.tree,as.factor(v.valid.df$y))
```
# Test Measures after Pruning the tree are as follows:
1. Accuracy : 78.9%
2. Sensitivity : 79.89% 
3. Specificity : 77.88% 

# Time Taken by Classification Tree Model to Execute
```{r}
t1 <- Sys.time()
cv.ct <- rpart(y~ ., data = t.train.df[-c(1:3,5,6,8,10)], method = "class", cp = 0.00001, minsplit = 5, xval = 5)
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```

# ROC Curve 
```{r}
tree.pred <- predict(pruned.ct, v.valid.df, type = "class")
table_data <- table(prediction = tree.pred,actual=v.valid.df$y)

# Accuracy Metric
sum(diag(table_data))/sum(table_data)

pred.tree <- predict(pruned.ct, v.valid.df, type = "prob")
par(pty = "s")
info <- roc(v.valid.df$y, pred.tree[,2],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Classification Tree Model",col="purple",lwd=3,print.auc=TRUE)
info
```
#2. Applying Classification Tree with selected predictors i.e.
1. poutcome
2. housing
3. loan
4. duration

```{r}
s_traindata <- t.train.df[c(7,8,12,16,17)]
s_validdata <- v.valid.df[c(7,8,12,16,17)]
s_testdata <- t.test.df[c(7,8,12,16,17)]
```
```{r}
class.tree <- rpart(y ~ .,data = s_traindata, control = rpart.control(maxdepth = 7), method = "class", minbucket = 50)
prp(class.tree, type = 1, extra = 1, under = TRUE, split.font = 1, varlen = -10)
```
```{r}
pred.tree <- predict(class.tree, s_validdata, type = "class")
confusionMatrix(pred.tree,as.factor(s_validdata$y))
```
# Cross Validation
```{r}
cv.ct <- rpart(y~ ., data = s_traindata, method = "class", cp = 0.00001, minsplit = 5, xval = 5)
printcp(cv.ct)
```
# Pruning the Tree
```{r}
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
length(pruned.ct$frame$var[pruned.ct$frame$var == "<leaf>"])
prp(pruned.ct, type = 1, extra = 1, split.font = 1, varlen = -10)
```
```{r}
pred.tree <- predict(pruned.ct, s_validdata, type = "class")
confusionMatrix(pred.tree,as.factor(s_validdata$y))
```
# Time Taken by Classification Tree Model to Execute
```{r}
t1 <- Sys.time()
cv.ct <- rpart(y~ ., data = s_traindata, method = "class", cp = 0.00001, minsplit = 5, xval = 5)
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```
# ROC Curve 
```{r}
pred.tree <- predict(pruned.ct, s_validdata, type = "prob")
par(pty = "s")
info <- roc(s_validdata$y, pred.tree[,2],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Classification Tree Model",col="purple",lwd=3,print.auc=TRUE)
info
```

# Applying Random Forest Model
```{r}
## random forest
rf <- randomForest(as.factor(y) ~ ., data = s_traindata, ntree = 100,
mtry = 4, nodesize = 5, importance = TRUE)
rf
```

```{r}
varImpPlot(rf, type = 1)
```
```{r}
rf.pred <- predict(rf, s_validdata)
confusionMatrix(rf.pred, s_validdata$y)
```
# Test Measures given by Random Forest Model are as follows:
1. Accuracy : 83.56%
2. Sensitivity : 82.33% 
3. Specificity : 84.81% 

# Time Taken by Random Forest Model to Execute
```{r}
t1 <- Sys.time()
rf <- randomForest(as.factor(y) ~ ., data = s_traindata, ntree = 100,
mtry = 4, nodesize = 5, importance = TRUE)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```

# ROC Curve 
```{r}
par(pty = "s")
info <- roc(s_traindata$y, rf$votes[,1],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Random Forest Model",col="#4daf4a",lwd=3,print.auc=TRUE)
info
```

# Boosting the Tree
```{r}
#install.packages('adabag')
s_traindata$y <- as.factor(s_traindata$y)
set.seed(1)
boost <- boosting(y ~ ., data = s_traindata)
pred <- predict(boost, s_validdata)
confusionMatrix(as.factor(pred$class), as.factor(s_validdata$y))
```
# Test Measures given after Boosting the tree are as follows:
1. Accuracy : 80.99%
2. Sensitivity : 80.83% 
3. Specificity : 81.15% 

# Time Taken by Random Forest Model to Execute After Boosting the Tree
```{r}
t1 <- Sys.time()
rf <- boost <- boosting(y ~ ., data = s_traindata)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```
# ROC Curve 
```{r}
par(pty = "s")
info <- roc(s_traindata$y, boost$votes[,1],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Boosted Tree",col="#4daf4a",lwd=3,print.auc=TRUE)
info
```

# Interpretation:
Based on the Accuracy and Confidence Interval, the following models seems convenient and will be used further.
1. KNN (with K=11)
2. Classification Tree(Pruned)
3. Random Forest Classification Model(Boosted)

Now we will implement these models on the test data with the relevant predictors

# 1. KNN Model
```{r}
knn.pred <- knn(train=ds_train[,-5],test = ds_test[,-5], cl =
                  ds_train$y, k=11)
accuracy.df <- confusionMatrix(table(knn.pred, ds_test$y))
accuracy.df
```
# ROC Curve
```{r}
knn.pred <- knn(train=ds_train[,-5],test = ds_test[,-5], cl =
                  ds_train$y, k=11,prob = TRUE)
scores.knn <- attr(knn.pred,"prob")

par(pty = "s")
info <- roc(ds_test$y, scores.knn,plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for KNN Model",col="orange",lwd=3,print.auc=TRUE)
info
```
# Time Taken by KNN Model to Execute
```{r}
t1 <- Sys.time()
knn.pred <- knn(train=ds_train[,-5],test = ds_test[,-5], cl =
                  ds_train$y, k=11)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```

#2. Classification Tree(Pruned) Model
```{r}
class.tree <- rpart(y ~ .,data = s_traindata, control = rpart.control(maxdepth = 7), method = "class", minbucket = 50)
prp(class.tree, type = 1, extra = 1, under = TRUE, split.font = 1, varlen = -10)
```
# Cross Validation
```{r}
cv.ct <- rpart(y~ ., data = s_traindata, method = "class", cp = 0.00001, minsplit = 5, xval = 5)
printcp(cv.ct)
```
# Pruning the Tree
```{r}
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
length(pruned.ct$frame$var[pruned.ct$frame$var == "<leaf>"])
prp(pruned.ct, type = 1, extra = 1, split.font = 1, varlen = -10)
```
```{r}
pred.tree <- predict(pruned.ct, s_testdata, type = "class")
confusionMatrix(pred.tree,as.factor(s_testdata$y))
```
# Time Taken by Classification Tree Model to Execute
```{r}
t1 <- Sys.time()
cv.ct <- rpart(y~ ., data = s_traindata, method = "class", cp = 0.00001, minsplit = 5, xval = 5)
pruned.ct <- prune(cv.ct, cp = cv.ct$cptable[which.min(cv.ct$cptable[,"xerror"]),"CP"])
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```
# ROC Curve 
```{r}
pred.tree <- predict(pruned.ct, s_testdata, type = "prob")
par(pty = "s")
info <- roc(s_testdata$y, pred.tree[,2],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Pruned Tree Model",col="purple",lwd=3,print.auc=TRUE)
info
```


# 3. Random Forest Classification Model(Boosted)
```{r}
rf <- randomForest(as.factor(y) ~ ., data = s_traindata, ntree = 100,
mtry = 4, nodesize = 5, importance = TRUE)
rf.pred <- predict(rf, s_testdata)
confusionMatrix(rf.pred, s_testdata$y)
```
# Boosting the Tree
```{r}
#install.packages('adabag')
s_traindata$y <- as.factor(s_traindata$y)
set.seed(1)
boost <- boosting(y ~ ., data = s_traindata)
pred <- predict(boost, s_testdata)
confusionMatrix(as.factor(pred$class), as.factor(s_testdata$y))
```
# ROC Curve 
```{r}
par(pty = "s")
info <- roc(s_traindata$y, boost$votes[,1],plot = TRUE,legacy.axes=TRUE,percent = TRUE, xlab="False Positive Percentage",ylab="True Positive Percentage" ,main = "ROC Curve for Boosted Tree",col="#4daf4a",lwd=3,print.auc=TRUE)
info
```
# Time Taken by Random Forest Model to Execute After Boosting the Tree
```{r}
t1 <- Sys.time()
rf <- boost <- boosting(y ~ ., data = s_traindata)
t2 <- Sys.time()
time_taken <- t2-t1
time_taken
```

# Interpretations:
1. Boosted tree gave the maximum Area Under Curve and best accuracy but also took maximum time to execute.
2. Pruned Tree also gave accuracy close to Boosted tree and 84.2% area under the curve. It also took very less time to execute as compared to the Boosted tree.
3. The KNN model took the least time to execute and a good accuracy but it gave worst Area Under Curve i.e 41.8%.

# Overall Boosted Tree seems to be the most convenient model to be used for this application.






